Two-Stage Automatic Liver Classification System Based on Deep Learning Approach Using CT Images.
Authors
Affiliations (5)
Affiliations (5)
- Department of Computer Engineering, Ataturk University, 10587, Erzurum, Turkey. [email protected].
- Department of Electrical Electronics Engineering, Ataturk University, 25240, Erzurum, Turkey. [email protected].
- Section of Radiology, Regional Education and Research Hospital, Ataturk University, 25240, Erzurum, Turkey.
- Department of Electrical Electronics Engineering, Ataturk University, 25240, Erzurum, Turkey.
- High Performance Department of Artificial Intelligence and Data Engineering, Erzurum, Turkey.
Abstract
Alveolar echinococcosis (AE) is a parasitic disease caused by Echinococcus multilocularis, where early detection is crucial for effective treatment. This study introduces a novel method for the early diagnosis of liver diseases by differentiating between tumor, AE, and healthy cases using non-contrast CT images, which are widely accessible and eliminate the risks associated with contrast agents. The proposed approach integrates an automatic liver region detection method based on RCNN followed by a CNN-based classification framework. A dataset comprising over 27,000 thorax-abdominal images from 233 patients, including 8206 images with liver tissue, was constructed and used to evaluate the proposed method. The experimental results demonstrate the importance of the two-stage classification approach. In a 2-class classification problem for healthy and non-healthy classes, an accuracy rate of 0.936 (95% CI: 0.925 <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>-</mo></math> 0.947) was obtained, and that for 3-class classification problem with AE, tumor, and healthy classes was obtained as 0.863 (95% CI: 0.847 <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>-</mo></math> 0.879). These results highlight the potential use of the proposed framework as a fully automatic approach for liver classification without the use of contrast agents. Furthermore, the proposed framework demonstrates competitive performance compared to other state-of-the-art techniques, suggesting its applicability in clinical practice.