Skin cancer remains a significant global health challenge,and early detection is crucial to improving patient outcomes.This study presents a novel deep learning framework that combines Convolutional Neural Networks(CN...Skin cancer remains a significant global health challenge,and early detection is crucial to improving patient outcomes.This study presents a novel deep learning framework that combines Convolutional Neural Networks(CNNs),Transformers,and Gated Recurrent Units(GRUs)for robust skin cancer classification.To address data set imbalance,we employ StyleGAN3-based synthetic data augmentation alongside traditional techniques.The hybrid architecture effectively captures both local and global dependencies in dermoscopic images,while the GRU component models sequential patterns.Evaluated on the HAM10000 dataset,the proposed model achieves an accuracy of 90.61%,outperforming baseline architectures such as VGG16 and ResNet.Our system also demonstrates superior precision(91.11%),recall(95.28%),and AUC(0.97),highlighting its potential as a reliable diagnostic tool for the detection of melanoma.This work advances automated skin cancer diagnosis by addressing critical challenges related to class imbalance and limited generalization in medical imaging.展开更多
Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting...Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task difficult.The recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics.Deep learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and treatment.In this work,we proposed a novel deep architecture for skin cancer classification in innovative healthcare.The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset.The proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter sizes.In the first model,four residual blocks are added in a squeezed fashion with a small filter size.In the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion region.To make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is selected.After training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique.The final features are passed on to the classifiers,and classification results are obtained.Also,the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset.The experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and ISIC2019.We obtained an improved accuracy of 90.8%and 99.3%on these datasets,respectively.Also,the proposed architecture returned 91.6%for the cancer localization.In conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art(SOTA)techniques and shows improved performance.展开更多
Oroxylin A(OA),a natural compound extracted from Scutellaria baicalensis,demonstrates preventive potential against ultraviolet B(UVB)-induced non-melanoma skin cancer(NMSC),the most prevalent cancer worldwide with inc...Oroxylin A(OA),a natural compound extracted from Scutellaria baicalensis,demonstrates preventive potential against ultraviolet B(UVB)-induced non-melanoma skin cancer(NMSC),the most prevalent cancer worldwide with increasing incidence.Utilizing SKH-1 hairless mice exposed to UVB,this study showed that OA delayed NMSC onset and alleviated acute skin damage.Mechanistic investigations revealed its dual action:inhibiting inflammation and enhancing nucleotide excision repair(NER)by stabilizing XPA,a crucial deoxyribonucleic acid(DNA)repair protein.This stabilization occurred through OA's interaction with glucose-regulated protein 94(GRP94),which disrupted murine double minute 2(MDM2)-mediated XPA ubiquitination and proteasomal degradation.By maintaining XPA levels,OA expedited photoproduct clearance and diminished genomic instability,ultimately impeding NMSC development.These findings suggest OA as a promising chemopreventive agent targeting the GRP94/MDM2-XPA axis to counteract UVB-induced carcinogenesis.展开更多
Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that...Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that are not regularly exposed to the sun.Due to the striking similarities between benign and malignant lesions,skin cancer detection remains a problem,even for expert dermatologists.Considering the inability of dermatologists to di-agnose skin cancer accurately,a convolutional neural network(CNN)approach was used for skin cancer diag-nosis.However,the CNN model requires a significant number of image datasets for better performance;thus,image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model,because there are a limited number of medical images.This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection:(i)actinic keratoses,(ii)basal cell carcinoma,(iii)benign keratosis,(iv)dermatofibroma,(v)melanocytic nevi,(vi)melanoma,and(vii)vascular skin lesions.Five transfer learning models were used as the basis of the ensemble:MobileNet,EfficientNetV2B2,Xception,ResNeXt101,and Den-seNet201.In addition to the stratified 10-fold cross-validation,the results of each individual model were fused to achieve greater classification accuracy.An annealing learning rate scheduler and test time augmentation(TTA)were also used to increase the performance of the model during the training and testing stages.A total of 10,015 publicly available dermoscopy images from the HAM10000(Human Against Machine)dataset,which contained samples from the seven common skin lesion categories,were used to train and evaluate the models.The proposed technique attained 94.49%accuracy on the dataset.These results suggest that this strategy can be useful for improving the accuracy of skin cancer classification.However,the weighted average of F1-score,recall,and precision were obtained to be 94.68%,94.49%,and 95.07%,respectively.展开更多
The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousa...The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.展开更多
Non-melanoma skin cancers or keratinocyte cancers such as basal cell carcinoma and squamous cell carcinoma make up approximately 80% and 20% respectively, of skin cancers with the 6 million people that are treated ann...Non-melanoma skin cancers or keratinocyte cancers such as basal cell carcinoma and squamous cell carcinoma make up approximately 80% and 20% respectively, of skin cancers with the 6 million people that are treated annually in the United States. 1 in 5 Americans and 2 in 3 Australians develop skin cancer by the age of 70 years and in Australia it is the most expensive, amassing $1.5 billion, to treat cancers. Non-melanoma skin cancers are often self-detected and are usually removed by various means in doctors’ surgeries. Mohs micrographic surgery is acclaimed to be the gold standard for the treatment of skin cancer. However, a novel microscopic molecular-cellular non-invasive topical therapy described in this article, challenges the status of Mohs procedure for being the acclaimed gold standard.展开更多
Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep lea...Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.展开更多
The incidences of nonmelanoma skin cancer are increasing worldwide, and the ongoing war on its treatment necessitates the development of effective and non-invasive methods. Through basic and clinical research, non-inv...The incidences of nonmelanoma skin cancer are increasing worldwide, and the ongoing war on its treatment necessitates the development of effective and non-invasive methods. Through basic and clinical research, non-invasive treatments like Curaderm have been developed, leading to improved quality of life for patients. Excipients, previously considered inactive ingredients, play a crucial role in enhancing the performance of topical formulations. The development of Curaderm emphasizes the importance of understanding the interactions between active ingredients, excipients, and the biological system to create effective and affordable pharmaceutical formulations. The systematic approach taken in the development of Curaderm, starting from the observation of the anticancer activity of natural solasodine glycosides and progressing through toxicological and efficacy studies in cell culture, animals, and humans, has provided insights into the pharmacokinetics and pharmacodynamics of solasodine glycosides. It is crucial to determine these pharmacological parameters within the skin’s biological system for maximal effectiveness and cost-effectiveness of a skin cancer treatment. Curaderm, as a topical treatment for nonmelanoma skin cancer, offers benefits beyond those obtained from other topical treatments, providing hope for improved quality of life for patients.展开更多
Basal cell carcinoma is the most common form of skin cancer and the most frequently occurring form of all cancers. Conventional treatments to remove or destroy basal cell carcinoma are indiscriminate and also remove o...Basal cell carcinoma is the most common form of skin cancer and the most frequently occurring form of all cancers. Conventional treatments to remove or destroy basal cell carcinoma are indiscriminate and also remove or destroy normal skin cells resulting in compromised cosmetic outcomes. Consequences of these treatments include body-image issues, anxiety, post-traumatic stress disorder, depression, and poorer quality of social and family life. A progressive topical cream formulation, Curaderm, containing the natural BEC glycoalkaloids, have shown to have advantages over conventional treatments. However, comprehensive clinical features of the skin cancer lesions during treatment with Curaderm have to date not been reported. This report shows that using unpublished data from a large number of patients with varying sizes, types and locations of basal cell carcinomas when treated with Curaderm in a phase 3 trial, an initial increase in size of the lesions occur, followed by a reverse course, leading to complete removal of the skin cancer. The specificity and mode of action of Curaderm explains the superior cosmetic outcomes when compared with conventional therapies.展开更多
Dermatologists typically require extensive experience to accurately classify skin cancer.In recent years,the development of computer vision and machine learning has provided new methods for assisted diagnosis.Existing...Dermatologists typically require extensive experience to accurately classify skin cancer.In recent years,the development of computer vision and machine learning has provided new methods for assisted diagnosis.Existing skin cancer image classification methods have certain limitations,such as poor interpretability,the requirement of domain knowledge for feature extraction,and the neglect of lesion area information in skin images.This paper proposes a new genetic programming(GP)approach to automatically learn global and/or local features from skin images for classification.To achieve this,a new function set and a new terminal set have been developed.The proposed GP method can automatically and flexibly extract effective local/global features from different types of input images,thus providing a comprehensive description of skin images.A new region detection function has been developed to select the lesion areas from skin images for feature extraction.The performance of this approach is evaluated on three skin cancer image classification tasks,and compared with three GP methods and six non-GP methods.The experimental results show that the new approach achieves significantly better or similar performance in most cases.Further analysis validates the effectiveness of our parameter settings,visualizes the multiple region detection functions used in the individual evolved by the proposed approach,and demonstrates its good convergence ability.展开更多
Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence technology.These developments are made to better the standard of med...Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence technology.These developments are made to better the standard of medical decision-making and the standard of treatment given to patients.Fuzzy boundaries,shifting sizes,and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images,slowing down the otherwise efficient process of diagnosing skin cancer.However,these difficulties may be conquered by employing image processing software.To address these issues,the authors of this paper provide a novel intelligent multilevel thresholding with deep learning(IMLT-DL)model for intelligent dermoscopic image processing.Multilevel thresholding and DL are brought together in this model.Top hat filtering and inpainting have been included into IMLT-DL for use in image processing.In addition,mayfly optimization has been used in tandem with multilayer Kapur’s thresholding to identify specific trouble spots.For further investigation,it uses an Inception v3-based feature extractor,and for data classification,it makes use of gradient boosting trees(GBTs).On the International Skin Imaging Collaboration(ISIC)dataset,this model was shown to outperform state-of-the-art alternatives by a margin of 0.992%over the duration of trial iterations.These advances are a major step forward in the quest for faster and more accurate skin lesion detection.展开更多
In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM1...In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 datasets.Initially,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image gradients.All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image.Later,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected datasets.The concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion images.In the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)technique.To avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)algorithm.The selected features are finally passed to the softmax classifier for the final classification.Three datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively.展开更多
Fluorescence lifetime(FLT)of fluorophores is sensitive to the changes in their surrounding microenvironment,and hence it can quantitatively reveal the physiological characterization of the tissue under investigation.F...Fluorescence lifetime(FLT)of fluorophores is sensitive to the changes in their surrounding microenvironment,and hence it can quantitatively reveal the physiological characterization of the tissue under investigation.Fluorescence lifetime imaging microscopy(FLIM)provides not only morphological but also functional information of the tisse by producing spatially resolved image of fuorophore lifetime,which can be used as a signature of disorder and/or malignancy in diseased tissues.In this paper,we begin by introducing the basic principle and common detection methods of FLIM.Then the recent advances in the FLIM-based diagnosis of three different skin cancers,including basal cell carcinoma(BCC),squamous cell carcinoma(SCC)and malignant melanoma(MM)are reviewed.Furthermore,the potential advantages of FLIM in skin cancer diagnosis and the challenges that may be faced in the future are prospected.展开更多
Diabetes and skin cancers have emerged as threats to public health worldwide.However,their association has been less intensively studied.In this narrative review,we explore the common risk factors,molecular mechanisms...Diabetes and skin cancers have emerged as threats to public health worldwide.However,their association has been less intensively studied.In this narrative review,we explore the common risk factors,molecular mechanisms,and prognosis of the association between cutaneous malignancies and diabetes.Hyperglycemia,oxidative stress,low-grade chronic inflammation,genetic,lifestyle,and environmental factors partially explain the crosstalk between skin cancers and this metabolic disorder.In addition,diabetes and its related complications may interfere with the appropriate management of cutaneous malignancies.Antidiabetic medication seems to exert an antineoplastic effect,however,future large,observation studies with a prospective design are needed to clarify its impact on the risk of malignancy in diabetes.Screening for diabetes in skin cancers,as well as close follow-up for the development of cutaneous malignancies in subjects suffering from diabetes,is warranted.展开更多
The worldwide mortality rate due to cancer is second only to cardiovascular diseases.The discovery of image processing,latest artificial intelligence techniques,and upcoming algorithms can be used to effectively diagn...The worldwide mortality rate due to cancer is second only to cardiovascular diseases.The discovery of image processing,latest artificial intelligence techniques,and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate.Efficiently applying these latest techniques has increased the survival chances during recent years.The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making.The datasets used for the experimentation and analysis are ISBI 2016,ISBI 2017,and HAM 10000.In this work pertained models are used to extract the efficient feature.The pertained models applied are ResNet,InceptionV3,and classical feature extraction techniques.Before that,efficient preprocessing is conducted on dermoscopic images by applying various data augmentation techniques.Further,for classification,convolution neural networks were implemented.To classify dermoscopic images on HAM 1000 Dataset,the maximum attained accuracy is 89.30%for the proposed technique.The other parameters for measuring the performance attained 87.34%(Sen),86.33%(Pre),88.44%(F1-S),and 11.30%false-negative rate(FNR).The class with the highest TP rate is 97.6%for Melanoma;whereas,the lowest TP rate was for the Dermatofibroma class.For dataset ISBI2016,the accuracy achieved is 97.0%with the proposed classifier,whereas the other parameters for validation are 96.12%(Sen),97.01%(Pre),96.3%(F1-S),and further 3.7%(FNR).For the experiment with the ISBI2017 dataset,Sen,Pre,F1-S,and FNR were 93.9%,94.9%,93.9%,and 5.2%,respectively.展开更多
The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization o...The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques.Nevertheless,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and scalability.To address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive attributes.The model utilizes a multi-scale feature extraction methodology by using various convolutional layers.The ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this study.Traditional data augmentation approaches are implemented to address the issue of model overfitting.In this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer detection.The findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of 0.89.Furthermore,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at 0.90.This model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.展开更多
<span style="font-family:Verdana;">Cancer cells can be proliferating in a few months and years</span><span style="font-family:Verdana;">.</span><span style="font-fam...<span style="font-family:Verdana;">Cancer cells can be proliferating in a few months and years</span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> It depends </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> cancer stage. Chemotherapy, immunotherapy and anti-metabolic drugs have been used in order to kill cancer cells and prevent immune system weakly and metastasis. However, such drugs can damage healthy cells too. Natural ways to cancer treatments may help whole body to cancer cells. In this work, it was taking off cancer nodule to skin cancer by surgery and we treat the nodule as wound, using Nanoskin</span><sup><span style="font-family:Verdana;"><sup></sup></span><span style="font-family:Verdana;background-color:#FFFFFF;"><sup><span style="font-family:Verdana, Helvetica, Arial;">®</sup></span></span></sup><span style="font-family:Verdana;"></span><span style="font-family:Verdana;"> advance cell therapy (ACT), natural extra cellular matrix which releases nutrients to the skin cancer. Our result shows that the cancer nodule disappear</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> in few weeks in skin, because of natural membrane treatment. In addition, we obtained complete wound healing due anticancer nutrients (beta-glucan) delivery to skin.</span>展开更多
Non-melanoma skin cancer(NMSC) is one of the most common cancers in the US, although the role of obesity in skin cancer remains unclear. In vivo studies have consistently demonstrated that obese mice challenged with U...Non-melanoma skin cancer(NMSC) is one of the most common cancers in the US, although the role of obesity in skin cancer remains unclear. In vivo studies have consistently demonstrated that obese mice challenged with UVB radiation show increased skin tumorigenesis in comparison with leaner control mice. Growing evidence suggests that enhanced inflammation, oxidative stress and impaired apoptosis may play important roles in the development of skin cancer. Interventions such as voluntary exercise and the surgical removal of parametrial fat have been demonstrated to be effective in reducing adipose tissue that may influence the development of skin cancer; however, these interventions are not achievable in all obese patients. Therefore, the use of dietary natural phytochemicals that may modify and reverse the deregulated molecular and epigenetic events related to obesity and cancer development might represent a potential therapeutic modality due to their potential efficacy and low toxicity. In this review, we aim to provide the molecular and epigenetic basis of the NMSC-obesity relationship and to highlight the potential anti-cancer chemopreventive benefits of dietary phytochemicals such as sulforaphane and epigallocatechin-3-gallate.展开更多
Melanoma or skin cancer is the most dangerous and deadliest disease.As the incidence and mortality rate of skin cancer increases worldwide,an automated skin cancer detection/classification system is required for early...Melanoma or skin cancer is the most dangerous and deadliest disease.As the incidence and mortality rate of skin cancer increases worldwide,an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer.In this study,a Hybrid Artificial Intelligence Model(HAIM)is designed for skin cancer classification.It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron(EWHMLP)for the classification.Though the wavelet transform is a powerful tool for signal and image processing,it is unable to detect the intermediate dimensional structures of a medical image.Thus the proposed HAIM uses Curvelet(CurT),Contourlet(ConT)and Shearlet(SheT)transforms as feature extraction techniques.Though MLP is very flexible and well suitable for the classification problem,the learning of weights is a challenging task.Also,the optimization process does not converge,and the model may not be stable.To overcome these drawbacks,EWHMLP is developed.Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33%in a multi-class approach on PH2 database.展开更多
Skin cancer rates have risen over the past decades,making it imperative that adults understand the need for protection from sun exposure.Though some risk factors have been identified as predictive for skin cancers,the...Skin cancer rates have risen over the past decades,making it imperative that adults understand the need for protection from sun exposure.Though some risk factors have been identified as predictive for skin cancers,there is a lack of synthesized information about factors that influence adults in their decisions to engage in sun protective behaviors.The purpose of this paper is to present the current state of the science on influential factors for sun protective behaviors in the general adult population.A rigorous literature search inclusive of a generally White,Caucasian,and non-Hispanic adult population was performed,and screening yielded 18 quantitative studies for inclusion in this review.Findings indicate that modifiable and non-modifiable factors are interdependent and play a role in sun protective behaviors.This study resulted in a proposed conceptual model for affecting behavioral change in sun protection including the following factors:personal characteristics,cognitive factors,family dynamics,and social/peer group influences.These factors are introduced to propose tailored nursing interventions that would change current sun protective behavior practice.Key implications for nursing research and practice focus on feasibility of annual skin cancer screening facilitated by advanced practice nurses,incorporating the identified influential factors to reduce skin cancer risk and unnecessary sun exposure.展开更多
文摘Skin cancer remains a significant global health challenge,and early detection is crucial to improving patient outcomes.This study presents a novel deep learning framework that combines Convolutional Neural Networks(CNNs),Transformers,and Gated Recurrent Units(GRUs)for robust skin cancer classification.To address data set imbalance,we employ StyleGAN3-based synthetic data augmentation alongside traditional techniques.The hybrid architecture effectively captures both local and global dependencies in dermoscopic images,while the GRU component models sequential patterns.Evaluated on the HAM10000 dataset,the proposed model achieves an accuracy of 90.61%,outperforming baseline architectures such as VGG16 and ResNet.Our system also demonstrates superior precision(91.11%),recall(95.28%),and AUC(0.97),highlighting its potential as a reliable diagnostic tool for the detection of melanoma.This work advances automated skin cancer diagnosis by addressing critical challenges related to class imbalance and limited generalization in medical imaging.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549)supported through Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R508)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task difficult.The recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics.Deep learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and treatment.In this work,we proposed a novel deep architecture for skin cancer classification in innovative healthcare.The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset.The proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter sizes.In the first model,four residual blocks are added in a squeezed fashion with a small filter size.In the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion region.To make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is selected.After training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique.The final features are passed on to the classifiers,and classification results are obtained.Also,the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset.The experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and ISIC2019.We obtained an improved accuracy of 90.8%and 99.3%on these datasets,respectively.Also,the proposed architecture returned 91.6%for the cancer localization.In conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art(SOTA)techniques and shows improved performance.
基金supported by the National Natural Science Foundation of China(No.81974425)the Natural Science Foundation of Jiangsu Province(Nos.BK20211578 and BK20210419)+1 种基金the China Postdoctoral Science Foundation Grant(No.2021M693513)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX22-0794)。
文摘Oroxylin A(OA),a natural compound extracted from Scutellaria baicalensis,demonstrates preventive potential against ultraviolet B(UVB)-induced non-melanoma skin cancer(NMSC),the most prevalent cancer worldwide with increasing incidence.Utilizing SKH-1 hairless mice exposed to UVB,this study showed that OA delayed NMSC onset and alleviated acute skin damage.Mechanistic investigations revealed its dual action:inhibiting inflammation and enhancing nucleotide excision repair(NER)by stabilizing XPA,a crucial deoxyribonucleic acid(DNA)repair protein.This stabilization occurred through OA's interaction with glucose-regulated protein 94(GRP94),which disrupted murine double minute 2(MDM2)-mediated XPA ubiquitination and proteasomal degradation.By maintaining XPA levels,OA expedited photoproduct clearance and diminished genomic instability,ultimately impeding NMSC development.These findings suggest OA as a promising chemopreventive agent targeting the GRP94/MDM2-XPA axis to counteract UVB-induced carcinogenesis.
文摘Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that are not regularly exposed to the sun.Due to the striking similarities between benign and malignant lesions,skin cancer detection remains a problem,even for expert dermatologists.Considering the inability of dermatologists to di-agnose skin cancer accurately,a convolutional neural network(CNN)approach was used for skin cancer diag-nosis.However,the CNN model requires a significant number of image datasets for better performance;thus,image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model,because there are a limited number of medical images.This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection:(i)actinic keratoses,(ii)basal cell carcinoma,(iii)benign keratosis,(iv)dermatofibroma,(v)melanocytic nevi,(vi)melanoma,and(vii)vascular skin lesions.Five transfer learning models were used as the basis of the ensemble:MobileNet,EfficientNetV2B2,Xception,ResNeXt101,and Den-seNet201.In addition to the stratified 10-fold cross-validation,the results of each individual model were fused to achieve greater classification accuracy.An annealing learning rate scheduler and test time augmentation(TTA)were also used to increase the performance of the model during the training and testing stages.A total of 10,015 publicly available dermoscopy images from the HAM10000(Human Against Machine)dataset,which contained samples from the seven common skin lesion categories,were used to train and evaluate the models.The proposed technique attained 94.49%accuracy on the dataset.These results suggest that this strategy can be useful for improving the accuracy of skin cancer classification.However,the weighted average of F1-score,recall,and precision were obtained to be 94.68%,94.49%,and 95.07%,respectively.
文摘The International Skin Imaging Collaboration(ISIC)datasets are pivotal resources for researchers in machine learning for medical image analysis,especially in skin cancer detection.These datasets contain tens of thousands of dermoscopic photographs,each accompanied by gold-standard lesion diagnosis metadata.Annual challenges associated with ISIC datasets have spurred significant advancements,with research papers reporting metrics surpassing those of human experts.Skin cancers are categorized into melanoma and non-melanoma types,with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated.This paper aims to address challenges in skin cancer detection via visual inspection and manual examination of skin lesion images,processes historically known for their laboriousness.Despite notable advancements in machine learning and deep learning models,persistent challenges remain,largely due to the intricate nature of skin lesion images.We review research on convolutional neural networks(CNNs)in skin cancer classification and segmentation,identifying issues like data duplication and augmentation problems.We explore the efficacy of Vision Transformers(ViTs)in overcoming these challenges within ISIC dataset processing.ViTs leverage their capabilities to capture both global and local relationships within images,reducing data duplication and enhancing model generalization.Additionally,ViTs alleviate augmentation issues by effectively leveraging original data.Through a thorough examination of ViT-based methodologies,we illustrate their pivotal role in enhancing ISIC image classification and segmentation.This study offers valuable insights for researchers and practitioners looking to utilize ViTs for improved analysis of dermatological images.Furthermore,this paper emphasizes the crucial role of mathematical and computational modeling processes in advancing skin cancer detection methodologies,highlighting their significance in improving algorithmic performance and interpretability.
文摘Non-melanoma skin cancers or keratinocyte cancers such as basal cell carcinoma and squamous cell carcinoma make up approximately 80% and 20% respectively, of skin cancers with the 6 million people that are treated annually in the United States. 1 in 5 Americans and 2 in 3 Australians develop skin cancer by the age of 70 years and in Australia it is the most expensive, amassing $1.5 billion, to treat cancers. Non-melanoma skin cancers are often self-detected and are usually removed by various means in doctors’ surgeries. Mohs micrographic surgery is acclaimed to be the gold standard for the treatment of skin cancer. However, a novel microscopic molecular-cellular non-invasive topical therapy described in this article, challenges the status of Mohs procedure for being the acclaimed gold standard.
文摘Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.
文摘The incidences of nonmelanoma skin cancer are increasing worldwide, and the ongoing war on its treatment necessitates the development of effective and non-invasive methods. Through basic and clinical research, non-invasive treatments like Curaderm have been developed, leading to improved quality of life for patients. Excipients, previously considered inactive ingredients, play a crucial role in enhancing the performance of topical formulations. The development of Curaderm emphasizes the importance of understanding the interactions between active ingredients, excipients, and the biological system to create effective and affordable pharmaceutical formulations. The systematic approach taken in the development of Curaderm, starting from the observation of the anticancer activity of natural solasodine glycosides and progressing through toxicological and efficacy studies in cell culture, animals, and humans, has provided insights into the pharmacokinetics and pharmacodynamics of solasodine glycosides. It is crucial to determine these pharmacological parameters within the skin’s biological system for maximal effectiveness and cost-effectiveness of a skin cancer treatment. Curaderm, as a topical treatment for nonmelanoma skin cancer, offers benefits beyond those obtained from other topical treatments, providing hope for improved quality of life for patients.
文摘Basal cell carcinoma is the most common form of skin cancer and the most frequently occurring form of all cancers. Conventional treatments to remove or destroy basal cell carcinoma are indiscriminate and also remove or destroy normal skin cells resulting in compromised cosmetic outcomes. Consequences of these treatments include body-image issues, anxiety, post-traumatic stress disorder, depression, and poorer quality of social and family life. A progressive topical cream formulation, Curaderm, containing the natural BEC glycoalkaloids, have shown to have advantages over conventional treatments. However, comprehensive clinical features of the skin cancer lesions during treatment with Curaderm have to date not been reported. This report shows that using unpublished data from a large number of patients with varying sizes, types and locations of basal cell carcinomas when treated with Curaderm in a phase 3 trial, an initial increase in size of the lesions occur, followed by a reverse course, leading to complete removal of the skin cancer. The specificity and mode of action of Curaderm explains the superior cosmetic outcomes when compared with conventional therapies.
基金supported in part by National Natural Science Foundation of China(U23A20340,62376253,62106230,62176238,62476254)China Postdoctoral Science Foundation,China(2023M743185)+2 种基金Natural Science Foundation of Henan Province,China(222300420088)Key Laboratory of Big Data Intelligent Computing,Chongqing University of Posts and Telecommunications,China Open Foundation(BDIC-2023-A-007)Frontier Exploration Projects of Longmen Laboratory,China(NO.LMQYTSKT031).
文摘Dermatologists typically require extensive experience to accurately classify skin cancer.In recent years,the development of computer vision and machine learning has provided new methods for assisted diagnosis.Existing skin cancer image classification methods have certain limitations,such as poor interpretability,the requirement of domain knowledge for feature extraction,and the neglect of lesion area information in skin images.This paper proposes a new genetic programming(GP)approach to automatically learn global and/or local features from skin images for classification.To achieve this,a new function set and a new terminal set have been developed.The proposed GP method can automatically and flexibly extract effective local/global features from different types of input images,thus providing a comprehensive description of skin images.A new region detection function has been developed to select the lesion areas from skin images for feature extraction.The performance of this approach is evaluated on three skin cancer image classification tasks,and compared with three GP methods and six non-GP methods.The experimental results show that the new approach achieves significantly better or similar performance in most cases.Further analysis validates the effectiveness of our parameter settings,visualizes the multiple region detection functions used in the individual evolved by the proposed approach,and demonstrates its good convergence ability.
文摘Over the past few years,the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by artificial intelligence technology.These developments are made to better the standard of medical decision-making and the standard of treatment given to patients.Fuzzy boundaries,shifting sizes,and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images,slowing down the otherwise efficient process of diagnosing skin cancer.However,these difficulties may be conquered by employing image processing software.To address these issues,the authors of this paper provide a novel intelligent multilevel thresholding with deep learning(IMLT-DL)model for intelligent dermoscopic image processing.Multilevel thresholding and DL are brought together in this model.Top hat filtering and inpainting have been included into IMLT-DL for use in image processing.In addition,mayfly optimization has been used in tandem with multilayer Kapur’s thresholding to identify specific trouble spots.For further investigation,it uses an Inception v3-based feature extractor,and for data classification,it makes use of gradient boosting trees(GBTs).On the International Skin Imaging Collaboration(ISIC)dataset,this model was shown to outperform state-of-the-art alternatives by a margin of 0.992%over the duration of trial iterations.These advances are a major step forward in the quest for faster and more accurate skin lesion detection.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the Soonchunhyang University Research Fund.
文摘In this work,we propose a new,fully automated system for multiclass skin lesion localization and classification using deep learning.The main challenge is to address the problem of imbalanced data classes,found in HAM10000,ISBI2018,and ISBI2019 datasets.Initially,we consider a pretrained deep neural network model,DarkeNet19,and fine-tune the parameters of third convolutional layer to generate the image gradients.All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network(HFaFFNN).The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image.Later,two pre-trained deep models,Darknet-53 and NasNet-mobile,are employed and fine-tuned according to the selected datasets.The concept of transfer learning is later explored to train both models,where the input feed is the generated localized lesion images.In the subsequent step,the extracted features are fused using parallel max entropy correlation(PMEC)technique.To avoid the problem of overfitting and to select the most discriminant feature information,we implement a hybrid optimization algorithm called entropy-kurtosis controlled whale optimization(EKWO)algorithm.The selected features are finally passed to the softmax classifier for the final classification.Three datasets are used for the experimental process,such as HAM10000,ISBI2018,and ISBI2019 to achieve an accuracy of 95.8%,97.1%,and 85.35%,respectively.
基金supported by The 111 Project(B17035)Open Research Fund Program of the State Key Laboratory of Low Dimensional Quantum Physics(KF201713)+1 种基金State Key Laboratory of Transient Optics and Photonics,Chinese Academy of Sciences(SKLST201804)the Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province(GD201711).
文摘Fluorescence lifetime(FLT)of fluorophores is sensitive to the changes in their surrounding microenvironment,and hence it can quantitatively reveal the physiological characterization of the tissue under investigation.Fluorescence lifetime imaging microscopy(FLIM)provides not only morphological but also functional information of the tisse by producing spatially resolved image of fuorophore lifetime,which can be used as a signature of disorder and/or malignancy in diseased tissues.In this paper,we begin by introducing the basic principle and common detection methods of FLIM.Then the recent advances in the FLIM-based diagnosis of three different skin cancers,including basal cell carcinoma(BCC),squamous cell carcinoma(SCC)and malignant melanoma(MM)are reviewed.Furthermore,the potential advantages of FLIM in skin cancer diagnosis and the challenges that may be faced in the future are prospected.
文摘Diabetes and skin cancers have emerged as threats to public health worldwide.However,their association has been less intensively studied.In this narrative review,we explore the common risk factors,molecular mechanisms,and prognosis of the association between cutaneous malignancies and diabetes.Hyperglycemia,oxidative stress,low-grade chronic inflammation,genetic,lifestyle,and environmental factors partially explain the crosstalk between skin cancers and this metabolic disorder.In addition,diabetes and its related complications may interfere with the appropriate management of cutaneous malignancies.Antidiabetic medication seems to exert an antineoplastic effect,however,future large,observation studies with a prospective design are needed to clarify its impact on the risk of malignancy in diabetes.Screening for diabetes in skin cancers,as well as close follow-up for the development of cutaneous malignancies in subjects suffering from diabetes,is warranted.
基金This research project was supported by a grant from the“Research Center of the Female Scientific and Medical Colleges,”Deanship of Scientific Research,King Saud University。
文摘The worldwide mortality rate due to cancer is second only to cardiovascular diseases.The discovery of image processing,latest artificial intelligence techniques,and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate.Efficiently applying these latest techniques has increased the survival chances during recent years.The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making.The datasets used for the experimentation and analysis are ISBI 2016,ISBI 2017,and HAM 10000.In this work pertained models are used to extract the efficient feature.The pertained models applied are ResNet,InceptionV3,and classical feature extraction techniques.Before that,efficient preprocessing is conducted on dermoscopic images by applying various data augmentation techniques.Further,for classification,convolution neural networks were implemented.To classify dermoscopic images on HAM 1000 Dataset,the maximum attained accuracy is 89.30%for the proposed technique.The other parameters for measuring the performance attained 87.34%(Sen),86.33%(Pre),88.44%(F1-S),and 11.30%false-negative rate(FNR).The class with the highest TP rate is 97.6%for Melanoma;whereas,the lowest TP rate was for the Dermatofibroma class.For dataset ISBI2016,the accuracy achieved is 97.0%with the proposed classifier,whereas the other parameters for validation are 96.12%(Sen),97.01%(Pre),96.3%(F1-S),and further 3.7%(FNR).For the experiment with the ISBI2017 dataset,Sen,Pre,F1-S,and FNR were 93.9%,94.9%,93.9%,and 5.2%,respectively.
文摘The early detection of skin cancer,particularly melanoma,presents a substantial risk to human health.This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques.Nevertheless,the existing methods exhibit certain constraints in terms of accessibility,diagnostic precision,data availability,and scalability.To address these obstacles,we put out a lightweight model known as Smart MobiNet,which is derived from MobileNet and incorporates additional distinctive attributes.The model utilizes a multi-scale feature extraction methodology by using various convolutional layers.The ISIC 2019 dataset,sourced from the International Skin Imaging Collaboration,is employed in this study.Traditional data augmentation approaches are implemented to address the issue of model overfitting.In this study,we conduct experiments to evaluate and compare the performance of three different models,namely CNN,MobileNet,and Smart MobiNet,in the task of skin cancer detection.The findings of our study indicate that the proposed model outperforms other architectures,achieving an accuracy of 0.89.Furthermore,the model exhibits balanced precision,sensitivity,and F1 scores,all measuring at 0.90.This model serves as a vital instrument that assists clinicians efficiently and precisely detecting skin cancer.
文摘<span style="font-family:Verdana;">Cancer cells can be proliferating in a few months and years</span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> It depends </span><span style="font-family:Verdana;">on</span><span style="font-family:Verdana;"> cancer stage. Chemotherapy, immunotherapy and anti-metabolic drugs have been used in order to kill cancer cells and prevent immune system weakly and metastasis. However, such drugs can damage healthy cells too. Natural ways to cancer treatments may help whole body to cancer cells. In this work, it was taking off cancer nodule to skin cancer by surgery and we treat the nodule as wound, using Nanoskin</span><sup><span style="font-family:Verdana;"><sup></sup></span><span style="font-family:Verdana;background-color:#FFFFFF;"><sup><span style="font-family:Verdana, Helvetica, Arial;">®</sup></span></span></sup><span style="font-family:Verdana;"></span><span style="font-family:Verdana;"> advance cell therapy (ACT), natural extra cellular matrix which releases nutrients to the skin cancer. Our result shows that the cancer nodule disappear</span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> in few weeks in skin, because of natural membrane treatment. In addition, we obtained complete wound healing due anticancer nutrients (beta-glucan) delivery to skin.</span>
基金Institutional funds,R01-CA118947 and R01-CA152826 from the National Cancer Institute(NCI)R01AT007065 from the National Center for Complementary and Alternative Medicines(NCCAM)and the Office of Dietary Supplements(ODS)
文摘Non-melanoma skin cancer(NMSC) is one of the most common cancers in the US, although the role of obesity in skin cancer remains unclear. In vivo studies have consistently demonstrated that obese mice challenged with UVB radiation show increased skin tumorigenesis in comparison with leaner control mice. Growing evidence suggests that enhanced inflammation, oxidative stress and impaired apoptosis may play important roles in the development of skin cancer. Interventions such as voluntary exercise and the surgical removal of parametrial fat have been demonstrated to be effective in reducing adipose tissue that may influence the development of skin cancer; however, these interventions are not achievable in all obese patients. Therefore, the use of dietary natural phytochemicals that may modify and reverse the deregulated molecular and epigenetic events related to obesity and cancer development might represent a potential therapeutic modality due to their potential efficacy and low toxicity. In this review, we aim to provide the molecular and epigenetic basis of the NMSC-obesity relationship and to highlight the potential anti-cancer chemopreventive benefits of dietary phytochemicals such as sulforaphane and epigallocatechin-3-gallate.
文摘Melanoma or skin cancer is the most dangerous and deadliest disease.As the incidence and mortality rate of skin cancer increases worldwide,an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer.In this study,a Hybrid Artificial Intelligence Model(HAIM)is designed for skin cancer classification.It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron(EWHMLP)for the classification.Though the wavelet transform is a powerful tool for signal and image processing,it is unable to detect the intermediate dimensional structures of a medical image.Thus the proposed HAIM uses Curvelet(CurT),Contourlet(ConT)and Shearlet(SheT)transforms as feature extraction techniques.Though MLP is very flexible and well suitable for the classification problem,the learning of weights is a challenging task.Also,the optimization process does not converge,and the model may not be stable.To overcome these drawbacks,EWHMLP is developed.Results show that the combined qualities of each transform in a hybrid approach provides an accuracy of 98.33%in a multi-class approach on PH2 database.
文摘Skin cancer rates have risen over the past decades,making it imperative that adults understand the need for protection from sun exposure.Though some risk factors have been identified as predictive for skin cancers,there is a lack of synthesized information about factors that influence adults in their decisions to engage in sun protective behaviors.The purpose of this paper is to present the current state of the science on influential factors for sun protective behaviors in the general adult population.A rigorous literature search inclusive of a generally White,Caucasian,and non-Hispanic adult population was performed,and screening yielded 18 quantitative studies for inclusion in this review.Findings indicate that modifiable and non-modifiable factors are interdependent and play a role in sun protective behaviors.This study resulted in a proposed conceptual model for affecting behavioral change in sun protection including the following factors:personal characteristics,cognitive factors,family dynamics,and social/peer group influences.These factors are introduced to propose tailored nursing interventions that would change current sun protective behavior practice.Key implications for nursing research and practice focus on feasibility of annual skin cancer screening facilitated by advanced practice nurses,incorporating the identified influential factors to reduce skin cancer risk and unnecessary sun exposure.
