Classification of Liver Diseases using CT and MR Images

Abstract

Over the last few decades, liver cancerous diseases have become a major leading cancer disease worldwide. The classes of liver cancer diseases enable further investigation with the diagnosis as the pre treatment. Early detection and diagnosis lessen the need for many implanted diagnostics, and receiving treatment sooner are advantageous. As a result, the current study was carried out to diagnose the many types of liver cancer diseases and their classifications, levels of tumor, and interpretation. The diagnosis of liver cancerous disorders is based on detecting and classifying the diseases: Hepatocellular carcinoma (HCC) and Metastases (MET). However, variability within the liver tumor portion allows a different class of intensities as the normal region and tumor region intensities mix-up. As a result, it is essential to perform the processing work that results in a varied tumor appearance in the images. An effective Computer Aided Diagnosis (CAD) system is designed to identify, classify, and interpret the level of liver tumor classes. We created a composite database that allows us to execute image processing work rapidly to complete the procedure. This database has a huge number of images for all tumor classes. The data images enable thorough analysis with all objectives work, resulting in the desired effect outcomes. In this research work, a composite database was created that included 4566 images from the CT and MR imaging domains for both the normal liver and the tumor region of the liver. Several classes in this composite database are described by the medical domain, including tumor classes and normal liver classes. There are 1957 abnormal CT and MR tumor images and 2609 normal CT and MR images. There are a total of 1054 abnormal MR tumor images and 2274 normal MR domain images.