Image Processing and Deep CNN-based Automatic Liver Cancer Detection - Gowtham V.; Thenmozhi (International Journal of Biomedical Innovations and Engineering, 2025)
Liver cancer ranks among the leading causes of mortality for people worldwide. In the current situation, manually identifying the cancer tissue is a challenging and timeconsuming task. Treatment planning, response monitoring, tumor load assessment, and prediction are all made possible by the segmentation of liver lesions in CT scans. To address the current problem of liver cancer, the Hybridized Fully Convolutional Neural Network (HFCNN), which has been theoretically modeled, has been proposed for liver tumor segmentation in this research. HFCNN has been a useful tool for liver cancer analysis in semantic segmentation. Making the distinction between cancerous lesions and those that are not is vital, even though the CT-based lesion-type characterization establishes the diagnosis and treatment plan. It requires resources, knowledge, and skills from highly qualified people. On the other hand, a thorough end-to-end learning strategy has been examined to aid in the differentiation of benign cysts from colorectal cancer metastases in abdominal CT scans of the liver. Our approach combines residual and pre-trained weights with the effective extraction of features from Inception. The original image voxel features have been consistently represented in feature maps, and the significance of features appears to reflect the most pertinent imaging criteria for each class. This deep learning system demonstrates the idea of illuminating certain decision-making stages of a pre-trained deep neural network by describing aspects that result in predictions and analyzing the inner layers.
Keywords: Deep Learning; Liver cancer detection; fully convolutional neural network; semantic segmentation; tumor segmentation