Hsin-Ya Su, Chung-Yueh Lien, Pai-Jung Huang, Woei-Chyn Chu
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A Practical Computer Aided Diagnosis System for Breast Ultrasound Classifying Lesions into the ACR BI-RADS Assessment
Purpose
In this paper, we propose an open-source deep learning-based computer-aided diagnosis system for breast ultrasound images based on the Breast Imaging Reporting and Data System (BI-RADS).
Methods
Our dataset with 8,026 region-of-interest images preprocessed with ten times data augmentation. We compared the classification performance of VGG-16, ResNet-50, and DenseNet-121 and two ensemble methods integrated the single models.
Results
The ensemble model achieved the best performance, with 81.8% accuracy. Our results show that our model is performant enough to classify Category 2 and Category 4/5 lesions, and data augmentation can improve the classification performance of Category 3.
Conclusion
Our main contribution is to classify breast ultrasound lesions into BI-RADS assessment classes that place more emphasis on adhering to the BI-RADS medical suggestions including recommending routine follow-up tracing (Category 2), short-term follow-up tracing (Category 3) and biopsies (Category 4/5).
期刊介绍：
The purpose of Journal of Medical and Biological Engineering, JMBE, is committed to encouraging and providing the standard of biomedical engineering. The journal is devoted to publishing papers related to clinical engineering, biomedical signals, medical imaging, bio-informatics, tissue engineering, and so on. Other than the above articles, any contributions regarding hot issues and technological developments that help reach the purpose are also included.
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