Advancing Liver Cancer Diagnosis: Innovative Deep Learning Algorithms for Segmentation and Classification

Liver cancer is a significant worldwide health issue that requires precise diagnosis techniques and efficient treatment options. This paper presents sophisticated deep learning (DL) algorithms for the segmentation and classification of liver cancers, providing a thorough comparative comparison. The advancements in DL have led to significant progress in other domains. As a result, there is a rising excitement for using DL techniques in liver research to aid clinicians in diagnosing and treating medical conditions. The Improved Fully Convolutional Neural Network (IFCNN) has been introduced in this study as a solution to the problem of liver tumor segmentation in cases of liver cancer. The IFCNN has been mathematically designed to address this issue. Transfer learning improves the ability to apply knowledge gained from one patient group to other patient groups with different characteristics. Comparative studies performed against ResNet and VGG16 demonstrate higher sensitivity, specificity, and overall classification metrics. The algorithms' efficacy and real-time processing capabilities have been extensively validated on multiple datasets, including different kinds and stages of liver cancer. This paves the way for eventual incorporation into clinical processes. These advanced algorithms exhibit superior precision and efficiency compared to current models, offering significant potential for improving clinical practice. They provide more dependable tools for precise diagnosis and informed treatment planning in the fight against liver cancer.