Corresponding Author

Ahmed El-Emam

Subject Area

Biomedical Engineering

Article Type

Original Study


It had been noticed that 3-phase and 4-phase computed tomography protocols with contrast serve as standard examinations for diagnosing liver tumors. Additionally, many patients require periodic follow-up, which entails significant 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 classification of liver lesions. The present work introduces a CNN-based module for the classification of liver lesions. The module consists of four stages: data acquisition, preprocessing, modelling, and evaluating. The proposed system has achieved an accuracy of 96% and 97% for 3-phase and 4-phase protocols, respectively. Moreover, it has been shown that the 3-phase protocol outperforms the 4-phase protocol, according to the dose report, with only a 1% loss of accuracy. However, this loss has not altered the multi-classification process. Thus, a three-phase protocol is recommended as a diagnostic tool for detecting focal liver lesions.


Deep learning, HCC, Small liver lesions, Liver cancer.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.