Title:Hounsfield Unit Variations-based Liver Lesions Detection and Classification
using Deep Learning
Volume: 20
Author(s): Anh-Cang Phan*, Thanh-Ngoan Trieu and Thuong-Cang Phan
Affiliation:
- Faculty of Information Technology, Vinh Long University of Technology Education, Vinh Long 85110, Vietnam
Keywords:
Liver lesions, Hounsfield units, Faster R-CNN, R-FCN, SSD, Mask R-CNN.
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.
