Accurate artificial intelligence method for abnormality detection of CT liver images

Abstract

Liver cancer is a leading cause of death worldwide and poses a significant challenge to physicians in terms of accurate diagnosis and treatment. AI-powered segmentation and classification algorithms can play a vital role in assisting physicians in detecting and diagnosing liver tumors. However, liver tumor classification is a difficult task due to factors such as noise, non-homogeneity, and significant appearance variations in cancerous tissue. In this study, we propose a novel approach to automatically segmenting and classifying liver tumors. Our proposed framework comprises three main components: a preprocessing unit to enhance picture contrast, a Masked Recurrent Convolutional Neural Network (RCNN) for liver segmentation, and a pixel-wise classification unit for identifying abnormalities in the liver. When our models are applied to the challenging MICCAI’2027 liver tumor segmentation (LITS) database, we achieve Dice similarity coefficients of 96% and 98% for liver segmentation and lesion identification, respectively. We also demonstrate the efficiency of our proposed framework by comparing it with similar strategies for tumor segmentations. The proposed approach achieved high accuracy, sensitivity, specificity, and F1 score parameters for liver segmentation and lesion identification. These results were evaluated using the Dice similarity coefficient and compared with similar strategies for tumor segmentation. Our approach holds promise for improving the accuracy and speed of liver tumor detection and diagnosis, which could have significant implications for patient outcomes.