ResNet50-Based Early Detection System for Alzheimer's Disease: Application of Deep Learning in Medical Image Diagnosis

This article introduces a deep learning web application project that uses the ResNet50 convolutional neural network to early detect and classify Alzheimer's disease from MRI brain scan images. The project aims to address the problems of strong subjectivity, high invasiveness, or complex interpretation in traditional diagnostic methods. By optimizing the ResNet50 model through transfer learning, it achieves end-to-end automatic diagnostic assistance, which has practical deployment and clinical application value.

Alzheimer's disease (AD) is the most common neurodegenerative disease. Among the approximately 55 million dementia patients worldwide, 60-70% are AD cases, placing a heavy burden on the healthcare system. Early diagnosis is crucial for intervention and treatment, but traditional methods rely on neuropsychological assessment, cerebrospinal fluid testing, or neuroimaging examinations, which have limitations such as strong subjectivity, invasiveness, or complex interpretation. The development of AI, especially deep learning technology, has brought revolutionary changes to medical image analysis, and CNN has become a powerful tool for auxiliary diagnosis.

The project uses ResNet50 as the core model, whose residual connections solve the gradient vanishing problem in deep networks, and it has strong feature extraction capabilities, convenience for transfer learning, and a balance between computational efficiency. Model adaptation strategies include: transfer learning initialization (using ImageNet pre-trained weights), replacing the classification head with AD stages (normal, mild cognitive impairment, mild/moderate AD), layered learning rate fine-tuning, and data augmentation (rotation, flipping, brightness adjustment, etc.). The MRI preprocessing process includes skull stripping, intensity normalization, spatial registration, slice selection, and size standardization.

Through Grad-CAM visualization, the model focuses on areas such as hippocampal atrophy, cortical thickness changes, ventricular enlargement, and temporal lobe structural abnormalities, which are highly consistent with the diagnostic basis of neuroradiologists. The system deployment adopts a web application architecture (responsive frontend interface, backend RESTful API, model services such as TensorFlow Serving), and optimizes performance through model quantization, batch processing inference, caching mechanisms, and asynchronous processing to achieve clinical real-time response.

System deployment faces challenges such as regulatory compliance (requiring FDA/CE/NMPA certification), doctor acceptance (needing interpretable results), data privacy (meeting HIPAA/GDPR), and robustness verification (diverse devices/patient groups). AI diagnostic systems need to enhance doctors' capabilities as an auxiliary role rather than replace them.

Current limitations include limited publicly labeled datasets, class imbalance, lack of longitudinal prediction capabilities, and no multimodal fusion. Future directions include: using 3D convolutional networks to utilize complete volume data, introducing attention mechanisms, federated learning for collaborative training under privacy protection, prognosis prediction of disease progression, and multimodal fusion (MRI+PET+clinical data) to build a comprehensive diagnostic system.

This project demonstrates the potential of deep learning in AD diagnosis, which can assist doctors in rapid and objective assessment, especially弥补ing the shortage of professional doctors in resource-poor areas. However, AI healthcare needs strict verification of safety, fairness, and interpretability, and algorithms are to enhance doctors' capabilities rather than replace them. With technological progress and improved regulation, AI will play an important role in early detection, precise treatment, and long-term management of AD, improving patients' quality of life and promoting the intelligentization of the medical industry.