Published Paper Details:

This Liver cancer remains one of the most significant causes of cancer-related mortality globally, necessitating fast and accurate diagnosis. Traditional diagnostic approaches, such as biopsies and imaging, are often invasive and time-consuming. Deep learning has emerged as a promising solution in the medical imaging domain due to its ability to automate feature extraction and deliver high accuracy. This study focuses on using Convolutional Neural Networks (CNNs) and other deep learning models to detect liver cancer from CT and MRI scans. The proposed framework incorporates feature extraction, classification, and segmentation to enhance diagnostic precision. Experimental results show that the deep learning models outperform traditional machine learning techniques in metrics such as accuracy, recall, and F1-score. The findings support the integration of AI-based systems to assist radiologists in early detection and improve patient outcomes.