Segmentation of Medical Liver Image Diseases Using Improved Contextual Convolutional Neural Network (COCOn) Model

Accurate segmentation of liver diseases from clinical pictures is pivotal for analysis and remedy planning. In this study, we introduce a more advantageous COCOn version for the proper delineation of liver pathologies. Leveraging convolutional neural networks (CNNs) and COCOn (Contextual Correlation Networks), our version integrates contextual facts to refine segmentation boundaries. Through good-sized experimentation on various clinical liver photo datasets, we display advanced overall performance as compared to trendy methods. Our advanced COCOn version is well-known for its robustness, efficiency, and adaptability, showcasing its promising ability for reinforcing clinical photo evaluation and scientific decision-making in liver disorder management.