Deep Learning Based Classification of Focal Liver Lesions with 3 and 4 Phase Contrast-Enhanced CT Protocols

It has been noticed that three-phase and four-phase computed tomography protocols with contrast serve as standard examinations for diagnosing liver tumors. Additionally, many patients require periodic follow-up, which entails signi fi cant radiation exposure for them. Advancements in image processing facilitate automated liver lesion segmentation. However, the challenge remains in classifying these small lesions by doctors, especially when the liver has different types of lesions with very little intensity difference. Therefore, deep learning can be utilized for the classi fi cation of liver lesions. The present work introduces a CNN-based module for the classi fi cation of liver lesions. The module consists of four stages: data acquisition, preprocessing, modeling, and evaluating. The proposed system has achieved an accuracy of 96 and 97% for three-phase and four-phase protocols, respectively. Moreover it has been shown that the three-phase protocol outperforms the four-phase protocol, according to the dose report, with only a 1% loss of accuracy. However, this loss has not altered the multiclassi fi cation process. Thus, a three-phase protocol is recommended as a diagnostic tool for detecting focal liver lesions.