The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study pre...The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study presents a novel Deformable Attention Vision Transformer(DA-ViT)architecture that integrates deformable Multi-Head Self-Attention(MHSA)with a Multi-Layer Perceptron(MLP)block for efficient classification of Alzheimer’s disease(AD)using Magnetic resonance imaging(MRI)scans.In contrast to traditional vision transformers,our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions,markedly diminishing processing demands while improving localized feature representation.DA-ViT contains only 0.93 million parameters,making it exceptionally suitable for implementation in resource-limited settings.We evaluate the model using a class-imbalanced Alzheimer’s MRI dataset comprising 6400 images across four categories,achieving a test accuracy of 80.31%,a macro F1-score of 0.80,and an area under the receiver operating characteristic curve(AUC)of 1.00 for the Mild Demented category.Thorough ablation studies validate the ideal configuration of transformer depth,headcount,and embedding dimensions.Moreover,comparison research indicates that DA-ViT surpasses state-of-theart pre-trained Convolutional Neural Network(CNN)models in terms of accuracy and parameter efficiency.展开更多
Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable...Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.展开更多
基金Prince Sattambin Abdulaziz University for funding this research work through the project number(PSAU/2025/R/1446).
文摘The early and precise identification of Alzheimer’s Disease(AD)continues to pose considerable clinical difficulty due to subtle structural alterations and overlapping symptoms across the disease phases.This study presents a novel Deformable Attention Vision Transformer(DA-ViT)architecture that integrates deformable Multi-Head Self-Attention(MHSA)with a Multi-Layer Perceptron(MLP)block for efficient classification of Alzheimer’s disease(AD)using Magnetic resonance imaging(MRI)scans.In contrast to traditional vision transformers,our deformable MHSA module preferentially concentrates on spatially pertinent patches through learned offset predictions,markedly diminishing processing demands while improving localized feature representation.DA-ViT contains only 0.93 million parameters,making it exceptionally suitable for implementation in resource-limited settings.We evaluate the model using a class-imbalanced Alzheimer’s MRI dataset comprising 6400 images across four categories,achieving a test accuracy of 80.31%,a macro F1-score of 0.80,and an area under the receiver operating characteristic curve(AUC)of 1.00 for the Mild Demented category.Thorough ablation studies validate the ideal configuration of transformer depth,headcount,and embedding dimensions.Moreover,comparison research indicates that DA-ViT surpasses state-of-theart pre-trained Convolutional Neural Network(CNN)models in terms of accuracy and parameter efficiency.
基金State Grid Jiangsu Electric Power Co.,Ltd.of the Science and Technology Project(Grant No.J2022004).
文摘Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.
