Automatic Classification of Diffuse Liver Diseases: Cirrhosis & Hepatosteatosis using Ultrasound Images

This research aims to develop an intelligent system that can detect and classify various liver diseases using machine learning. This study will employ digital Ultra-sound images (US) collected from various hospitals and clinics in Egypt, as it is identified as one of the most common and affordable methods of medical imaging in the country [1], to identify liver diseases in a non-invasive manner. We will be extracting multiple Gray-Level Correlation Matrix (GLCM), Gray Level Run Length Matrix (GLRLM) and First Order Statistical (FOS) features from each US. Multiple feature selection methods will be used to select the most important features for each of the 3 binary classifiers. The trained classifiers achieved an accuracy of 89.7 %, 84.2 % and 95.5% for the Normal/Steatosis, Normal/Cirrhosis and Steatosis/Cirrhosis classifiers respectively. A majority function was used to classify the US based on the result of the 3 binary classifiers, the total accuracy of the model was 85.3%.