Medimagepathway: A Scalable Framework for Advanced Segmentation in Medical Image Processing

Medical image segmentation is a crucial task in computer-aided diagnosis and treatment planning. In this study, we propose MedImagePathway, a scalable and highly accurate deep learning-based framework for medical image segmentation, integrating attention mechanisms, transformerbased refinement, and multi-scale convolutional learning. The framework is evaluated on multiple benchmark datasets across different imaging modalities, including MRI, CT, X-ray, and Ultrasound. The proposed model achieves a Dice Similarity Coefficient (DSC) of 94.1%, an Intersection over Union (IoU) of 88.6 %, and an overall accuracy of 98.81 %, significantly outperforming state-of-the-art models such as U-Net, DeepLabV3+, Attention U-Net, and Swin-Unet. Computational efficiency is optimized, with a training time of 2.5 minutes per epoch, an inference speed of 35 ms per image, and a GPU memory usage of 6.8 GB, making it feasible for real-time clinical applications. Post-processing techniques, including Conditional Random Fields (CRF) and GAN-based superresolution enhancement, further refine segmentation accuracy. This study demonstrates the potential of MedImagePathway as a robust and efficient segmentation model for various medical imaging applications, including brain tumor segmentation, cardiac MRI analysis, lung disease detection, and dermatological lesion identification. The proposed framework can be further extended for federated learning, self-supervised learning, and real-time Edge AI deployment in clinical settings.