A Multi-Task Attention-Driven SegNet for Lung Infection Segmentation and Classification From HRCT Images

This project builds a multi-task deep learning framework using MS-CSTA-Net and DualMask-SegNet v5.4 to jointly segment and classify lung infections from HRCT images via multi-scale attention and dual mask learning. A web interface allows image upload, segmentation map viewing, and classification results, with performance evaluated using accuracy, precision, recall, F1-score, and IoU. The scalable system adapts to diverse datasets, reduces manual effort, and offers interpretable infection visualizations to support medical imaging research.