A Hybrid Multi-Scale CNN–Transformer Framework with Cross-Attention Fusion for Alzheimer’s Disease Diagnosis

A Hybrid Multi-Scale CNN–Transformer Framework with Cross-Attention Fusion for Alzheimer’s Disease Diagnosis project presents a hybrid deep learning framework for the automated classification of Alzheimer’s disease using MRI brain images. The proposed system combines convolutional neural networks and transformer architectures to effectively capture both local structural details and global contextual information from medical images. Two advanced classification models are developed and evaluated: DCT-MRI Net, which utilizes dual CNN and Transformer encoders with a Cross-Attention Fusion mechanism to integrate complementary features, and MSF-S2T Net, which employs a multi-scale convolutional backbone followed by a Transformer encoder with Cross-Scale Attention to enhance feature representation across different image resolutions. Comprehensive preprocessing techniques are applied to improve data quality and consistency before model training. A classification head predicts the disease category based on the extracted features. The system is implemented through a Flask-based web application that enables users to upload MRI images and obtain classification results. By combining multi-scale learning, attention mechanisms, and feature fusion strategies, the framework aims to improve diagnostic accuracy, robustness, and efficiency for Alzheimer’s disease classification.