Hounsfield Unit Variations-Based Liver Lesions Detection and Classification Using Deep Learning

Anh-Cang Phan; Thanh Ngoan Trieu; Thuong Cang Phan

doi:10.2174/1573405620666230428121748

Hounsfield Unit Variations-Based Liver Lesions Detection and Classification Using Deep Learning

Curr Med Imaging. 2023 Apr 28. doi: 10.2174/1573405620666230428121748. Online ahead of print.

Authors

Anh-Cang Phan¹, Thanh Ngoan Trieu^{2

3}, Thuong Cang Phan²

Affiliations

¹ Faculty of Information Technology, Vinh Long University of Technology Education, 85110 Vinh Long, Vietnam.
² College of Information and Communication Technology, Can Tho University, 94115 Can Tho, Vietnam.
³ La Faculté Sciences et Techniques, Université de Bretagne Occidentale, 29200 Brest, France.

PMID: 37132318
DOI: 10.2174/1573405620666230428121748

Abstract

Background: Deep learning-based diagnosis systems are useful to identify abnormalities in medical images with the greatly increased workload of doctors. Specifically, the rate of new cases and deaths from malignancies is rising for liver diseases. Early detection of liver lesions plays an extremely important role in effective treatment and gives a higher chance of survival for patients. Therefore, automatic detection and classification of common liver lesions are essential for doctors. In fact, radiologists mainly rely on Hounsfield Units to locate liver lesions but previous studies often pay little attention to this factor.

Methods: In this paper, we propose an improved method for the automatic classification of common liver lesions based on deep learning techniques and the variation of Hounsfield Unit densities on CT images with and without contrast. Hounsfield Unit is used to locate liver lesions accurately and support data labeling for classification. We construct a multi-phase classification model developed on the deep neural networks of Faster R-CNN, R-FCN, SSD, and Mask R-CNN with the transfer learning approach.

Results: The experiments are conducted on six scenarios with multi-phase CT images of common liver lesions. Experimental results show that the proposed method improves the detection and classification of liver lesions compared with recent methods because its accuracy achieves up to 97.4%.

Conclusion: The proposed models are very useful to assist doctors in the automatic segmentation and classification of liver lesions to solve the problem of depending on the clinician's experience in the diagnosis and treatment of liver lesions.

Keywords: Faster R-CNN; Hounsfield Units; Liver lesions; Mask R-CN Liver lesions; Mask R-CNN; R-FCN; SSD.