This research aims to develop an intelligent system that can detect and classify various liver diseases using image analysis and machine learning methods. This study will employ Ultrasound (US) imaging, which has been identified as one of the most common and affordable methods of medical imaging in Egypt [1] to identify liver diseases in a non-invasive manner. However, due to its low price, the images may be of poor quality or lacking in detail. As a result, a feature-based technique will be used to correctly categorise the US photographs, and after completing significant research, most efficient classifier (such as KNN, ANN, or SVM) will be selected to utilise in the classification phase.

• To create a reliable system with a score of 80 or better on the System Usability Scale (SUS) [3].

• Designing and creating an autonomous digital system for the human liver that analyses and classifies digital Ultarsound (US) images.

• Classify the diffuse liver diseases (Cirrhosis and Steatosis) and classify which stage is the illness in.

• The application should have each patients’ medical information for the doctor to access

Our proposed system uses an ultrasound image to detect the three types of liver diseases (Cirrhosis, Steatosis, and Normal). Our classification system will detect the abnormality faster than a traditional diagnosis.

The system will:

• Classify whether the liver ultrasound is normal or abnormal.

• Classify the type of abnormality (Cirrhosis or Steatosis).

• Classify the stage of the abnormality (Mild, Moderate and Severe).

• Generate a patient report.

• Abstain from providing a classification if the model thinks the result is inaccurate.