Fatty Liver Detection Using CNN
Fatty liver disease, also known as hepatic steatosis, is a condition where fat builds up in the liver. Early detection and classification of the severity are critical for medical treatment. In this report, we use a Convolutional Neural Network (CNN) to classify liver ultrasound images into four categories: Normal Mild Moderate Severe The goal is to leverage CNN’s ability to detect complex patterns in medical imaging to accurately classify the severity of fatty liver.
Dataset
The dataset consists of liver ultrasound images labeled with the following classes: Normal: Healthy liver with no fat accumulation. Mild: Early stage of fat accumulation. Moderate: Moderate accumulation of fat in the liver. Severe: Advanced stage of liver fat accumulation.
Results
The CNN model demonstrates high accuracy in detecting and classifying the severity of fatty liver disease based on ultrasound images. The results show that the model performs well in distinguishing between normal, mild, moderate, and severe fatty liver cases, with an accuracy of 100%.
