A Deep Learning Framework for Early Detection of Hepatocellular Carcinoma and Alcoholic Liver Disease

Abstract

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality worldwide, and early detection remains a significant challenge due to overlapping clinical symptoms and complex disease etiology. This study proposes a deep learning–based diagnostic framework for the classification of hepatocellular carcinoma into viral and non-viral categories using Convolutional Neural Networks (CNNs). The dataset used in this study consists of medical imaging data (CT/MRI scans) along with associated clinical parameters collected from publicly available biomedical repositories. The proposed model incorporates preprocessing techniques such as normalization and resizing, followed by automated feature extraction using CNN architecture. The model is trained using the Adam optimizer and categorical cross entropy loss function. Experimental results demonstrate that the proposed approach achieves an accuracy of 93.8%, with improved precision, recall, and F1-score compared to traditional machine learning models. The results highlight the effectiveness of deep learning in medical image analysis and its potential as a computer-aided diagnostic tool for early and accurate classification of HCC.