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

Anh-Cang Phan¹, Thanh Ngoan Trieu^2,3, Thuong Cang Phan²

¹Faculty of Information Technology, Vinh Long University of Technology Education, 85110 Vinh Long, Vietnam.

Current Medical Imaging

|May 3, 2023

Summary

This summary is machine-generated.

This study introduces an improved deep learning method for automatic liver lesion classification using Hounsfield Unit variations in CT scans. The system achieves up to 97.4% accuracy, aiding doctors in early liver disease detection.

Keywords:

Faster R-CNN Hounsfield Units Liver lesions Mask R-CN Liver lesions Mask R-CNN R-FCN SSD

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Rising incidence of liver malignancies necessitates advanced diagnostic tools.
Early detection of liver lesions significantly improves patient survival rates.
Current diagnostic methods often overlook Hounsfield Unit variations.

Purpose of the Study:

To develop an automated system for liver lesion detection and classification.
To integrate Hounsfield Unit analysis into deep learning models for improved accuracy.
To assist clinicians in diagnosing and treating liver lesions.

Main Methods:

Utilized deep learning techniques, including Faster R-CNN, R-FCN, SSD, and Mask R-CNN.
Incorporated Hounsfield Unit density variations from contrast-enhanced and non-contrast CT images.
Employed a transfer learning approach for model development.

Main Results:

Achieved up to 97.4% accuracy in detecting and classifying common liver lesions.
Demonstrated improved performance compared to existing methods across six experimental scenarios.
Validated the effectiveness of Hounsfield Unit integration for lesion localization and data labeling.

Conclusions:

The developed deep learning models effectively aid in automatic liver lesion segmentation and classification.
The system reduces reliance on subjective clinical experience for diagnosis.
Offers a valuable tool for enhancing the accuracy and efficiency of liver disease management.