AI-Powered Medical Image Analysis: Enhancing Radiology with Deep Learning-Based Image Segmentation

It is quite important nowadays due to allowing radiologists to outline different structures and pathological areas in images using computer assistance. This work aims at improving the segmentation in MRI, CT, X-ray, histopathology images with the multi-modal segmentation model based on CNN-Transformer architecture. To enhance the segmentation accuracy, the introduced model combines CNNs for local features and transformer-based self-attention for capturing long-range dependencies. Evaluation of the models on the assessment datasets presents a DSC of 94.2% and IoU of 88.4% that shows the superiority of the proposed architecture over other models such as U-Net, Attention U-Net, and Swin- UNET. In terms of performance across all the imaging modalities the DSC scores are 93.6% for MRI, 92.8% for CT, 90.5% for x-ray and 95.2% for histopathology, which corroborates the strength of the model. The understanding of the role of Transformer encoders and use of attention mechanisms is supported through an ablation study that shows an improvement in segmentation quality. Even though its computations are slightly higher than the previous model, the higher accuracy of the proposed model makes it more clinically relevant to real life applications. This work offer an efficient methodology supported by AI oriented at improving the efficiency of radiology with the least content expertise. Therefore the next steps involve the use of explainable AI and optimization during model deployment in a bid to enhance the interpretability of the models as well as the inference time.