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Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization.

Authors
  • Hizukuri, Akiyoshi1
  • Nakayama, Ryohei2
  • Nara, Mayumi3
  • Suzuki, Megumi3
  • Namba, Kiyoshi3
  • 1 Department of Electronic and Computer Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577,, Japan. [email protected] , (Japan)
  • 2 Department of Electronic and Computer Engineering, Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, 525-8577,, Japan. , (Japan)
  • 3 Department of Breast Surgery, Hokuto Hospital, 7-5 Kisen, Inada-cho, Obihiro-shi, Hokkaido, 080-0833,, Japan. , (Japan)
Type
Published Article
Journal
Journal of Digital Imaging
Publisher
Springer-Verlag
Publication Date
Feb 01, 2021
Volume
34
Issue
1
Pages
116–123
Identifiers
DOI: 10.1007/s10278-020-00394-2
PMID: 33159279
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Although magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, the specificity is lower. The purpose of this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses on dynamic contrast material-enhanced MRI (DCE-MRI) by using a deep convolutional neural network (DCNN) with Bayesian optimization. Our database consisted of 56 DCE-MRI examinations for 56 patients, each of which contained five sequential phase images. It included 26 benign and 30 malignant masses. In this study, we first determined a baseline DCNN model from well-known DCNN models in terms of classification performance. The optimum architecture of the DCNN model was determined by changing the hyperparameters of the baseline DCNN model such as the number of layers, the filter size, and the number of filters using Bayesian optimization. As the input of the proposed DCNN model, rectangular regions of interest which include an entire mass were selected from each of DCE-MRI images by an experienced radiologist. Three-fold cross validation method was used for training and testing of the proposed DCNN model. The classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 92.9% (52/56), 93.3% (28/30), 92.3% (24/26), 93.3% (28/30), and 92.3% (24/26), respectively. These results were substantially greater than those with the conventional method based on handcrafted features and a classifier. The proposed DCNN model achieved high classification performance and would be useful in differential diagnoses of masses in breast DCE-MRI images as a diagnostic aid.

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