Abstract

The utilization of machine learning optimization methods is of utmost importance in the identification of liver tumors, attracting considerable interest in this domain. After obtaining a liver tissue sample, magnetic resonance imaging (MRI), computed tomography (CT), and ultrasonography (US) are used as imaging techniques to separate the tumor and liver. Nevertheless, the utilization of shades of gray and forms is insufficient for achieving accurate segmentation in computed abdominal CT images, mostly because of the presence of overlapping intensities and the unpredictable placements and shapes of soft tissues. The results demonstrate that our proposed technique outperforms previous state-of-the-art models in terms of overall accuracy in tumor identification. The 2D Convolutional Neural Network (CNN) model had a remarkable training accuracy of 96.47%, while the auto-encoder network closely followed with an accuracy of 95.63%. In addition, the 2D CNN network exhibited an impressive average recall rate of 95%, beating the auto-encoder network's rate of 94%. The ROC curve regions for both networks exhibited remarkable performance, with values ranging from 0.99 to 1. Out of the many machine learning approaches used, the ResUNet had the least accurate results, while the K-Nearest Neighbors (KNN) achieved the greatest accuracy rate of 86%. Conversely, the MLP demonstrated a paltry accuracy rate of about 28%. The statistical tests conducted in this study indicated a significant difference (p-value < 0.05) between the suggested approach and many existing machine learning algorithms.