Advancing Early Disease Detection through Convolutional Neural Network Architectures in Medical Image Analysis

Abstract Alzheimer's disease (AD) is a common neurodegenerative disease, and its early detection is of great significance for delaying the progression of the disease and improving the quality of life of patients. Traditional clinical diagnostic methods rely on neuropsychological tests and imaging analysis, which are highly subjective, costly, and time-consuming. In recent years, convolutional neural networks (CNNs) have shown great potential in the field of medical image analysis due to their excellent feature extraction capabilities. This study proposes a CNN-based method for early detection of Alzheimer's disease. A deep learning model is trained using MRI imaging data to automatically learn lesion-related features, and classification experiments are performed. By comparing the performance of different CNN structures such as VGG, ResNet, and DenseNet, as well as the effects of 5-fold and 10-fold cross-validation strategies on the generalization ability of the model, the experimental results show that DenseNet121 performs best in the classification task, and 10-fold cross-validation can improve the stability of the model. The research results verify the feasibility of CNN in early detection of Alzheimer's disease, and provide research directions for combining multimodal data and optimizing deep learning models in the future.