Despite the revolution of deep learning in medical image analysis, multi-disease diagnoses still fail in the presence of noise, artifacts, and the resolution constraint. An innovative deep auto-diagnosis system which incorporates a Self Supervised Denoising and Enhancement Network (SSDE – Net) with a Multi Attention Disease Detection Network (MADD – Net) is proposed to improve diagnostic accuracy in this study. To generate semantically meaningful enhanced image, SSDE-Net applies the contrastive learning with Denoising Diffusion Probabilistic Models (DDPM) and MADD-Net introduces a hybrid CNN-Transformer network with cross fusion attention to refine multi-task disease detection. Furthermore, a Graph Neural Network (GNN)-based multi-modal fusion incorporates patient metadata, Clinical reports to perform the context-aware decision making. Finally, the proposed framework is evaluated on various datasets, and shown superior performance compared to other approaches especially in a low resolution setting. Experimental results show improved classifying disease in the experimental results as well as better generalizability across imaging modalities and model interpretability with attention-based visualization. For privacy preservation, future advancement will be made to the federated learning, for computational efficiency, quantum machine learning will be in the spotlight, while multi-modal fusion will be towards better diagnosis altogether. The results reported in this research serve as a basis for more accurate AI-driven healthcare approaches with high diagnostic precision and within the limiting medical environments.
Volume 11 | Issue 3
Pages: 106-111