AlzCNN-TL: An Alzheimer's Disease MRI Detection System Based on Transfer Learning

This graduation project proposes the AlzCNN-TL system, which uses convolutional neural network (CNN) transfer learning technology to automatically detect Alzheimer's disease from brain MRI images. The system aims to address challenges such as scarce labeled data and class imbalance in medical image analysis, provide clinical auxiliary diagnostic tools, improve diagnostic efficiency and consistency, and has future development directions like multi-modal fusion.

Alzheimer's disease is the most common neurodegenerative disease, with over 55 million patients worldwide. Early diagnosis is crucial for delaying the progression of the disease. Traditional diagnosis relies on clinical evaluation and cognitive tests, which are highly subjective and prone to missed diagnoses. Medical images such as MRI provide the possibility of objectively quantifying brain structural changes, but they face challenges such as scarce labeled data, the need for professional doctors' participation, and severe class imbalance. Deep learning training from scratch requires massive data, which is difficult to meet in the medical field.

The core idea of transfer learning is to use large-scale general datasets to pre-train models and fine-tune them to adapt to specific medical tasks. This project adopts a CNN architecture combined with transfer learning strategies:

1. Data preprocessing: Intensity normalization, skull stripping, spatial registration, etc., to ensure image comparability;
2. Transfer learning strategy: Use a pre-trained CNN as a feature extractor, freeze the underlying parameters to retain general visual knowledge, and only train the top classifier to avoid overfitting;
3. Model architecture: CNN captures spatial patterns through local connections and weight sharing, and stacks convolutional layers to learn hierarchical features (from low-level edges to high-level anatomical structures).

The clinical significance of this system lies in providing an auxiliary diagnostic tool that automatically screens MRI images and marks suspicious areas for doctors to review, improving diagnostic efficiency and consistency. As a 'second pair of eyes', it reduces the burden on radiologists (but cannot replace doctors' judgments). Future directions include multi-modal fusion, longitudinal analysis, and enhanced interpretability. Combining clinical data and genetic information is expected to achieve more accurate risk stratification and prognosis prediction.

This project is a typical medical AI graduation project, showing the complete path of applying cutting-edge deep learning technology to clinical problems (problem definition → data preparation → model design → result evaluation). The transfer learning strategy to improve model performance in data-constrained scenarios is particularly worth learning, providing practical references for learners.