Cancer Symptoms Detection from Liver CT Images Using Multistage Pre-Processors

Abstract

Visually cancer is the abnormal pattern with predefined structure could be found in liver Computed Tomography (CT) images. Using deep convolution neural network computation and image processing, this detected abnormal pattern cluster can be classified in different liver issue types. Full size liver CT scan images consisting different body parts, and these are ultrasonic based gray scaled image construction. The primary challenge in the cancer symptoms detection process is to extract the liver area out of image then finding out the actual area of abnormality to conclude whether abnormality is cancer or any other issues on liver. This is two stage processes, first is to segment the abnormality area and second is to perform pattern matching to identify the abnormality. This research paper primarily focuses on different pre-processing techniques and stages involved in liver abnormality segmentation.