Detection of Liver Cirrhosis using a Web-Based Convolutional Neural Network System

Liver cirrhosis, a critical health condition marked by irreversible scarring of the liver, contributes significantly to global morbidity and mortality. Traditional diagnostic methods are invasive, costly, and often detect the disease at advanced stages. This study presents the design and implementation of a non-invasive, web-based liver cirrhosis detection system employing Convolutional Neural Networks (CNNs). The system aims to support early diagnosis and prognosis using medical imaging and machine learning. The implemented system demonstrates high accuracy in classifying cirrhotic conditions and offers a scalable solution for under-resourced medical facilities