Liver Tumor Segmentation and Classification Using Deep Learning Mechanisms

The diagnosis of liver diseases from the CT images is a complex task for radiologists due to the difficulty in distinguishing tumor characteristics and tumor types. In this work, we introduced a deep-learning framework which is the process of identifying liver tumors from the CT images followed by classifying the tumors into either HCC or CC. The step of segmentation separates the tumor from the liver tissue; hence, one can analyze them with better clarity and reduce the effect of irrelevant anatomical structures. The segregated tumor is now categorized by key features, which determines the type of carcinoma. Therefore, a two-stage process of this detection will help in enhancing diagnostic accuracy and bring about the necessary critical analysis of tumor characteristics. The model will help healthcare professionals identify liver tumors better, hence yielding earlier diagnosis, proper treatment planning, and better patient outcomes.