A deep learning framework for supporting the classification of breast lesions in ultrasound images. (15th September 2017)

• Record Type: Journal Article
• Authors: Han, Seokmin; Kang, Ho-Kyung; Jeong, Ja-Yeon; Park, Moon-Ho; Kim, Wonsik; Bang, Won-Chul; Seong, Yeong-Kyeong
• Is Part Of: Physics in medicine & biology. Volume 62:Number 19(2017:Oct.)
• Page Start: 7714
• Page End: 7728
• Publication Date: 2017-09-15
• Subjects: deep learning -- CADx -- classification -- breast cancer; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aa82ec
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.