The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)images.This study introduces Dynamic GradNet,a novel deep learning mode...The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)images.This study introduces Dynamic GradNet,a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification.Initially,four state-of-the-art convolutional neural network(CNN)architectures,the self-regulated network(RegNet),residual network(ResNet),densely connected convolutional network(DenseNet),and efficient network(EfficientNet),were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation.Among these models,EfficientNet consistently demonstrated superior performance in terms of accuracy,precision,recall,and F1 score.As a result,EfficientNetwas selected as the foundation for implementing Dynamic GradNet.Dynamic GradNet incorporates gradient weighted class activation mapping(GradCAM)into the training process,facilitating dynamic adjustments that focus on critical brain regions associated with early dementia detection.These adjustments are particularly effective in identifying subtle changes associated with very mild dementia,enabling early diagnosis and intervention.The model was evaluated with the OASIS dataset,which contains greater than 80,000 brain MR images categorized into four distinct stages of AD progression.The proposed model outperformed the baseline architectures,achieving remarkable generalizability across all stages.This findingwas especially evident in early-stage dementia detection,where Dynamic GradNet significantly reduced false positives and enhanced classification metrics.These findings highlight the potential of Dynamic GradNet as a robust and scalable approach for AD diagnosis,providing a promising alternative to traditional attention-based models.The model’s ability to dynamically adjust spatial focus offers a powerful tool in artificial intelligence(AI)assisted precisionmedicine,particularly in the early detection of neurodegenerative diseases.展开更多
Background:Skin cancer poses a significant global health threat,with early detection being essential for successful treatment.While deep learning algorithms have greatly enhanced the categorization of skin lesions,the...Background:Skin cancer poses a significant global health threat,with early detection being essential for successful treatment.While deep learning algorithms have greatly enhanced the categorization of skin lesions,the black-box nature of many models limits interpretability,posing challenges for dermatologists.Methods:To address these limitations,SkinSage XAI utilizes advanced explainable artificial intelligence(XAI)techniques for skin lesion categorization.A data set of around 50,000 images from the Customized HAM10000,selected for diversity,serves as the foundation.The Inception v3 model is used for classification,supported by gradient-weighted class activation mapping and local interpretable model-agnostic explanations algorithms,which provide clear visual explanations for model outputs.Results:SkinSage XAI demonstrated high performance,accurately categorizing seven types of skin lesions—dermatofibroma,benign keratosis,melanocytic nevus,vascular lesion,actinic keratosis,basal cell carcinoma,and melanoma.It achieved an accuracy of 96%,with precision at 96.42%,recall at 96.28%,F1 score at 96.14%,and an area under the curve of 99.83%.Conclusions:SkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability.The system provides transparent,accurate diagnoses,improving decision-making for dermatologists and potentially enhancing patient outcomes.展开更多
Selecting techniques is a crucial aspect of disease detection analysis,particularly in the convergence of computer vision and agricultural technology.Maintaining crop disease detection in a timely and accurate manner ...Selecting techniques is a crucial aspect of disease detection analysis,particularly in the convergence of computer vision and agricultural technology.Maintaining crop disease detection in a timely and accurate manner is essential to maintaining global food security.Deep learning is a viable answer to meet this need.To proceed with this study,we have developed and evaluated a disease detection model using a novel ensemble technique.We propose to introduce DenseNetMini,a smaller version of DenseNet.We propose combining DenseNetMini with a learning resizer in ensemble approach to enhance training accuracy and expedite learning.Another unique proposition involves utilizing Gradient Product(GP)as an optimization technique,effectively reducing the training time and improving the model performance.Examining images at different magnifications reveals noteworthy diagnostic agreement and accuracy improvements.Test accuracy rates of 99.65%,98.96%,and 98.11%are seen in the Plantvillage,Tomato leaf,and Appleleaf9 datasets,respectively.One of the research's main achievements is the significant decrease in processing time,which suggests that using the GP could improve disease detection in agriculture's accessibility and efficiency.Beyond quantitative successes,the study highlights Explainable Artificial Intelligence(XAl)methods,which are essential to improving the disease detection model's interpretability and transparency.XAI enhances the interpretability of the model by visually identifying critical areas on plant leaves for disease identification,which promotes confidence and understanding of the model's functionality.展开更多
基金funded by Taif University,Saudi ArabiaThe author would like to acknowledge Deanship of Graduate Studies and Scientific Research,Taif University for funding this work.
文摘The evolving field of Alzheimer’s disease(AD)diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance(MR)images.This study introduces Dynamic GradNet,a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification.Initially,four state-of-the-art convolutional neural network(CNN)architectures,the self-regulated network(RegNet),residual network(ResNet),densely connected convolutional network(DenseNet),and efficient network(EfficientNet),were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation.Among these models,EfficientNet consistently demonstrated superior performance in terms of accuracy,precision,recall,and F1 score.As a result,EfficientNetwas selected as the foundation for implementing Dynamic GradNet.Dynamic GradNet incorporates gradient weighted class activation mapping(GradCAM)into the training process,facilitating dynamic adjustments that focus on critical brain regions associated with early dementia detection.These adjustments are particularly effective in identifying subtle changes associated with very mild dementia,enabling early diagnosis and intervention.The model was evaluated with the OASIS dataset,which contains greater than 80,000 brain MR images categorized into four distinct stages of AD progression.The proposed model outperformed the baseline architectures,achieving remarkable generalizability across all stages.This findingwas especially evident in early-stage dementia detection,where Dynamic GradNet significantly reduced false positives and enhanced classification metrics.These findings highlight the potential of Dynamic GradNet as a robust and scalable approach for AD diagnosis,providing a promising alternative to traditional attention-based models.The model’s ability to dynamically adjust spatial focus offers a powerful tool in artificial intelligence(AI)assisted precisionmedicine,particularly in the early detection of neurodegenerative diseases.
文摘Background:Skin cancer poses a significant global health threat,with early detection being essential for successful treatment.While deep learning algorithms have greatly enhanced the categorization of skin lesions,the black-box nature of many models limits interpretability,posing challenges for dermatologists.Methods:To address these limitations,SkinSage XAI utilizes advanced explainable artificial intelligence(XAI)techniques for skin lesion categorization.A data set of around 50,000 images from the Customized HAM10000,selected for diversity,serves as the foundation.The Inception v3 model is used for classification,supported by gradient-weighted class activation mapping and local interpretable model-agnostic explanations algorithms,which provide clear visual explanations for model outputs.Results:SkinSage XAI demonstrated high performance,accurately categorizing seven types of skin lesions—dermatofibroma,benign keratosis,melanocytic nevus,vascular lesion,actinic keratosis,basal cell carcinoma,and melanoma.It achieved an accuracy of 96%,with precision at 96.42%,recall at 96.28%,F1 score at 96.14%,and an area under the curve of 99.83%.Conclusions:SkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability.The system provides transparent,accurate diagnoses,improving decision-making for dermatologists and potentially enhancing patient outcomes.
文摘Selecting techniques is a crucial aspect of disease detection analysis,particularly in the convergence of computer vision and agricultural technology.Maintaining crop disease detection in a timely and accurate manner is essential to maintaining global food security.Deep learning is a viable answer to meet this need.To proceed with this study,we have developed and evaluated a disease detection model using a novel ensemble technique.We propose to introduce DenseNetMini,a smaller version of DenseNet.We propose combining DenseNetMini with a learning resizer in ensemble approach to enhance training accuracy and expedite learning.Another unique proposition involves utilizing Gradient Product(GP)as an optimization technique,effectively reducing the training time and improving the model performance.Examining images at different magnifications reveals noteworthy diagnostic agreement and accuracy improvements.Test accuracy rates of 99.65%,98.96%,and 98.11%are seen in the Plantvillage,Tomato leaf,and Appleleaf9 datasets,respectively.One of the research's main achievements is the significant decrease in processing time,which suggests that using the GP could improve disease detection in agriculture's accessibility and efficiency.Beyond quantitative successes,the study highlights Explainable Artificial Intelligence(XAl)methods,which are essential to improving the disease detection model's interpretability and transparency.XAI enhances the interpretability of the model by visually identifying critical areas on plant leaves for disease identification,which promotes confidence and understanding of the model's functionality.
