In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under...In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases.Magnetic Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging modality.This technique is commonly utilized by healthcare professionals for Brain Tumor(BT)diagnosis.With recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided design.The current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of BT.The proposed DLBTDC-MRI technique involves medianfiltering technique to remove the noise and enhance the quality of MRI images.Besides,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI image.Moreover,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature vectors.Finally,Artificial Fish Swarm Optimization(AFSO)with Artificial Neural Network(ANN)model is utilized as a classifier to decide the presence of BT.In order to assess the enhanced BT classification performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.展开更多
Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer a...Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist radiologists.With the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided design.This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer classification.The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors.The proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI images.In addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image.Moreover,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature extractors.Finally,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain tumors.For examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.展开更多
Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining th...Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.展开更多
Gold clay is a crafting medium consisting of gold particles mixed with an organic binder and water for making jewelry or decoration.The clay can be shaped by hand,textured,carved,formed or using molds.After drying and...Gold clay is a crafting medium consisting of gold particles mixed with an organic binder and water for making jewelry or decoration.The clay can be shaped by hand,textured,carved,formed or using molds.After drying and burning,the organic binder and water were decomposed and the gold particles were transformed to its final metal state.Although,gold clay is very expensive,it is useful to decorate the silver clay designed jewelry or small sculptures.In this research,nano-microplate gold and specific organic binder was used for producing nano-microplate gold clay.The objectives of this research are to study binder's type and ratios for optimum producing gold clay,and to study the heating condition for making silver and gold clay jewelry.The result showed that the clay can be fired with heating temperature at 900°C for an hour by electric kiln.The physical properties of the gold clay at different heating temperatures were determined.Furthermore,prototype of jewelry using the clay was展开更多
On a global scale,lung cancer is responsible for around 27%of all cancer fatalities.Even though there have been great strides in diagnosis and therapy in recent years,the five-year cure rate is just 19%.Classification...On a global scale,lung cancer is responsible for around 27%of all cancer fatalities.Even though there have been great strides in diagnosis and therapy in recent years,the five-year cure rate is just 19%.Classification is crucial for diagnosing lung nodules.This is especially true today that automated categorization may provide a professional opinion that can be used by doctors.New computer vision and machine learning techniques have made possible accurate and quick categorization of CT images.This field of research has exploded in popularity in recent years because of its high efficiency and ability to decrease labour requirements.Here,they want to look carefully at the current state of automated categorization of lung nodules.Generalpurpose structures are briefly discussed,and typical algorithms are described.Our results show deep learning-based lung nodule categorization quickly becomes the industry standard.Therefore,it is critical to pay greater attention to the coherence of the data inside the study and the consistency of the research topic.Furthermore,there should be greater collaboration between designers,medical experts,and others in the field.展开更多
Competition in modern society requires teachers to cultivate creative and pioneering talents, which indicates that teachers are required to cultivate students' creative ability. In the primary school stage, art ha...Competition in modern society requires teachers to cultivate creative and pioneering talents, which indicates that teachers are required to cultivate students' creative ability. In the primary school stage, art handicraft is to cultivate students' practical ability and interest, while in the primary school stage, and art handicraft teaching mostly belongs to the teaching of cultivating students' creative ability. This article will discuss the art handicraft teaching and the primary school students' creative ability cultivation countermeasure.展开更多
I've always been interested in traditional crafts,so when my school organized a workshop on wood carving,an ancient Chinese art form,I signed up right away.The workshop was held in a small studio in the old town.A...I've always been interested in traditional crafts,so when my school organized a workshop on wood carving,an ancient Chinese art form,I signed up right away.The workshop was held in a small studio in the old town.As I walked in,I was greeted by the smell of wood and the sound made by carving wood.The teacher,Mr Zhang,was a master carver with over 30 years of experience.He showed us some of his works-beautiful sculptures of animals and flowers.I was amazed by the skill and patience it must have taken to create them.展开更多
Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learni...Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.展开更多
基金supported through the Annual Funding track by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Project No.AN000684].
文摘In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases.Magnetic Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging modality.This technique is commonly utilized by healthcare professionals for Brain Tumor(BT)diagnosis.With recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided design.The current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of BT.The proposed DLBTDC-MRI technique involves medianfiltering technique to remove the noise and enhance the quality of MRI images.Besides,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI image.Moreover,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature vectors.Finally,Artificial Fish Swarm Optimization(AFSO)with Artificial Neural Network(ANN)model is utilized as a classifier to decide the presence of BT.In order to assess the enhanced BT classification performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.
基金This research work was funded by Institutional fund projects under grant no.(IFPHI-180-612-2020)Therefore,the authors gratefully acknowledge technical and financial support from the Ministry of Education and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography(CT),or magnetic resonance imaging(MRI).An automated brain cancer classification using computer aided diagnosis(CAD)models can be designed to assist radiologists.With the recent advancement in computer vision(CV)and deep learning(DL)models,it is possible to automatically detect the tumor from images using a computer-aided design.This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features(HGSO-FHDF)technique for brain cancer classification.The proposed HGSO-FHDF technique aims for detecting and classifying different stages of brain tumors.The proposed HGSO-FHDF technique involves Gabor filtering(GF)technique for removing the noise and enhancing the quality of MRI images.In addition,Tsallis entropy based image segmentation approach is applied to determine injured brain regions in the MRI image.Moreover,a fusion of handcrafted with deep features using Residual Network(ResNet)is utilized as feature extractors.Finally,HGSO algorithm with kernel extreme learning machine(KELM)model was utilized for identifying the presence of brain tumors.For examining the enhanced brain tumor classification performance,a comprehensive set of simulations take place on the BRATS 2015 dataset.
文摘Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.
文摘Gold clay is a crafting medium consisting of gold particles mixed with an organic binder and water for making jewelry or decoration.The clay can be shaped by hand,textured,carved,formed or using molds.After drying and burning,the organic binder and water were decomposed and the gold particles were transformed to its final metal state.Although,gold clay is very expensive,it is useful to decorate the silver clay designed jewelry or small sculptures.In this research,nano-microplate gold and specific organic binder was used for producing nano-microplate gold clay.The objectives of this research are to study binder's type and ratios for optimum producing gold clay,and to study the heating condition for making silver and gold clay jewelry.The result showed that the clay can be fired with heating temperature at 900°C for an hour by electric kiln.The physical properties of the gold clay at different heating temperatures were determined.Furthermore,prototype of jewelry using the clay was
文摘On a global scale,lung cancer is responsible for around 27%of all cancer fatalities.Even though there have been great strides in diagnosis and therapy in recent years,the five-year cure rate is just 19%.Classification is crucial for diagnosing lung nodules.This is especially true today that automated categorization may provide a professional opinion that can be used by doctors.New computer vision and machine learning techniques have made possible accurate and quick categorization of CT images.This field of research has exploded in popularity in recent years because of its high efficiency and ability to decrease labour requirements.Here,they want to look carefully at the current state of automated categorization of lung nodules.Generalpurpose structures are briefly discussed,and typical algorithms are described.Our results show deep learning-based lung nodule categorization quickly becomes the industry standard.Therefore,it is critical to pay greater attention to the coherence of the data inside the study and the consistency of the research topic.Furthermore,there should be greater collaboration between designers,medical experts,and others in the field.
文摘Competition in modern society requires teachers to cultivate creative and pioneering talents, which indicates that teachers are required to cultivate students' creative ability. In the primary school stage, art handicraft is to cultivate students' practical ability and interest, while in the primary school stage, and art handicraft teaching mostly belongs to the teaching of cultivating students' creative ability. This article will discuss the art handicraft teaching and the primary school students' creative ability cultivation countermeasure.
文摘I've always been interested in traditional crafts,so when my school organized a workshop on wood carving,an ancient Chinese art form,I signed up right away.The workshop was held in a small studio in the old town.As I walked in,I was greeted by the smell of wood and the sound made by carving wood.The teacher,Mr Zhang,was a master carver with over 30 years of experience.He showed us some of his works-beautiful sculptures of animals and flowers.I was amazed by the skill and patience it must have taken to create them.
文摘Recognizing road scene context from a single image remains a critical challenge for intelligent autonomous driving systems,particularly in dynamic and unstructured environments.While recent advancements in deep learning have significantly enhanced road scene classification,simultaneously achieving high accuracy,computational efficiency,and adaptability across diverse conditions continues to be difficult.To address these challenges,this study proposes HybridLSTM,a novel and efficient framework that integrates deep learning-based,object-based,and handcrafted feature extraction methods within a unified architecture.HybridLSTM is designed to classify four distinct road scene categories—crosswalk(CW),highway(HW),overpass/tunnel(OP/T),and parking(P)—by leveraging multiple publicly available datasets,including Places-365,BDD100K,LabelMe,and KITTI,thereby promoting domain generalization.The framework fuses object-level features extracted using YOLOv5 and VGG19,scene-level global representations obtained from a modified VGG19,and fine-grained texture features captured through eight handcrafted descriptors.This hybrid feature fusion enables the model to capture both semantic context and low-level visual cues,which are critical for robust scene understanding.To model spatial arrangements and latent sequential dependencies present even in static imagery,the combined features are processed through a Long Short-Term Memory(LSTM)network,allowing the extraction of discriminative patterns across heterogeneous feature spaces.Extensive experiments conducted on 2725 annotated road scene images,with an 80:20 training-to-testing split,validate the effectiveness of the proposed model.HybridLSTM achieves a classification accuracy of 96.3%,a precision of 95.8%,a recall of 96.1%,and an F1-score of 96.0%,outperforming several existing state-of-the-art methods.These results demonstrate the robustness,scalability,and generalization capability of HybridLSTM across varying environments and scene complexities.Moreover,the framework is optimized to balance classification performance with computational efficiency,making it highly suitable for real-time deployment in embedded autonomous driving systems.Future work will focus on extending the model to multi-class detection within a single frame and optimizing it further for edge-device deployments to reduce computational overhead in practical applications.
