Deep Learning-Enabled Real-Time Medical Image Segmentation: A Multi-Modal Approach for Diagnostic Accuracy

Deep learning is an innovative tool, which positively impacted medical image segmentation improving the diagnostic processes. In this paper, a novel Real-time medical image segmentation method using deep learning is proposed that uses CNNs with attention mechanism along with methods of self-supervision. As for methods based on the use of tomographic data, we have developed a new method that enhances the accuracy of tissue classification and the identification of anomalies by 20-30%, compared to the individual modality used. As for the validation, percentages presented show that it is possible to achieve up to 15% greater segmentation accuracy compared to traditional approaches. The above study reveals the capability and advantage of DL-based segmentation for performing prompt diagnosis without compromising the system qualities and medical credence.