Dynamic Spatial Focus in Alzheimer’s Disease Diagnosis via Multiple CNN Architectures and Dynamic GradNet  

作　　者：Jasem Almotiri 

机构地区：[1]Department of Computer Science,College of Computers and Information Technology,Taif University,Taif,21944,Saudi Arabia

出　　处：《Computers, Materials & Continua》2025年第5期2109-2142,共34页计算机、材料和连续体(英文)

基　　金：funded by Taif University,Saudi Arabia;The author would like to acknowledge Deanship of Graduate Studies and Scientific Research,Taif University for funding this work.

摘　　要：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.

关 键 词：Spatial focus GradCAM medical image classification deep learning early dementia detection neurodegenerative disease MRI analysis Alzheimer’s attention CNN 

分 类 号：R74[医药卫生—神经病学与精神病学]