Translator Disclaimer
Paper
22 May 2020 Computerized classification scheme for distinguishing between benign and malignant masses by analyzing multiple MRI sequences with convolutional neural network
Author Affiliations +
Proceedings Volume 11513, 15th International Workshop on Breast Imaging (IWBI2020); 115131Z (2020) https://doi.org/10.1117/12.2564061
Event: Fifteenth International Workshop on Breast Imaging, 2020, Leuven, Belgium
Abstract
Breast magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, but the specificity is lower. In MRI examination at clinical practice, multiple MRI sequences are usually acquired to achieve high diagnostic accuracy. The purpose of this study was to develop a computerized classification scheme for distinguishing between benign and malignant masses by integrally analyzing multiple MRI sequences with convolutional neural networks (CNNs). Our database consisted of four MRI sequences for 43 patients with masses. It included T1-weighted images, T2- weighted images, dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) images, and the difference images of the DCE-MRI images for each patient. In training the CNNs, the CNNs were first trained independently for each MRI sequence. The CNN features extracted from four MRI sequences with the trained CNNs were then inputted to a support vector machine (SVM) for distinguishing between benign and malignant masses. A k-fold cross validation method (k=3) was used for training and testing the CNNs and the SVM. With the proposed method, the classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 88.4% (38/43), 90.0% (27/30), 84.6% (11/13), 78.6% (11/14), and 93.1% (27/29), respectively. The classification performance with the proposed method analyzing multiple MRI sequences was substantially greater than those with CNNs analyzing one MRI sequence. The proposed method achieved high classification performance and would be useful in differential diagnoses of masses as diagnostic aid.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuichi Mima, Akiyoshi Hizukuri, and Ryohei Nakayama "Computerized classification scheme for distinguishing between benign and malignant masses by analyzing multiple MRI sequences with convolutional neural network", Proc. SPIE 11513, 15th International Workshop on Breast Imaging (IWBI2020), 115131Z (22 May 2020); https://doi.org/10.1117/12.2564061
PROCEEDINGS
7 PAGES


SHARE
Advertisement
Advertisement
Back to Top