Experimental Investigations to Detection of Liver Cancer Using ResUNet

Abstract

The detection and identification of cancerous tissue is currently a time-consuming and challenging process. The segmentation of liver lesions from cancer CT images can aid in treatment planning and clinical response monitoring. This study employs Residual U-Net, a powerful tool that has been adapted and applied for the segmentation of liver tumors, addressing the ongoing challenge in liver cancer diagnosis. Segmentation of liver lesions in CT images can be utilized to assess tumor burden, predict therapeutic outcomes, and monitor clinical response. In this research, the liver was extracted from the CT image using ResUNet, and the tumor was subsequently segmented using another ResUNet applied to the extracted Region of Interest (ROI). This approach effectively extracts features from Inception by combining residual and pre-trained weights. The deep learning system elucidates the underlying concept by highlighting the components contributing to the inner layer analysis and prediction, and by revealing a section of the decision-making process employed by pre-trained deep neural networks.