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Diagnostics 2018, 8(3), 48; https://doi.org/10.3390/diagnostics8030048

Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network

Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
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Received: 28 May 2018 / Revised: 18 July 2018 / Accepted: 23 July 2018 / Published: 25 July 2018
(This article belongs to the Special Issue Computer-Aided Diagnosis and Characterization of Diseases)
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Abstract

It can be difficult for clinicians to accurately discriminate among histological classifications of breast lesions on ultrasonographic images. The purpose of this study was to develop a computer-aided diagnosis (CADx) scheme for determining histological classifications of breast lesions using a convolutional neural network (CNN). Our database consisted of 578 breast ultrasonographic images. It included 287 malignant (217 invasive carcinomas and 70 noninvasive carcinomas) and 291 benign lesions (111 cysts and 180 fibroadenomas). In this study, the CNN constructed from four convolutional layers, three batch-normalization layers, four pooling layers, and two fully connected layers was employed for distinguishing between the four different types of histological classifications for lesions. The classification accuracies for histological classifications with our CNN model were 83.9–87.6%, which were substantially higher than those with our previous method (55.7–79.3%) using hand-crafted features and a classifier. The area under the curve with our CNN model was 0.976, whereas that with our previous method was 0.939 (p = 0.0001). Our CNN model would be useful in differential diagnoses of breast lesions as a diagnostic aid. View Full-Text
Keywords: convolutional neural network; histological classification; computer-aided diagnosis; breast lesion; ultrasonographic image convolutional neural network; histological classification; computer-aided diagnosis; breast lesion; ultrasonographic image
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Hizukuri, A.; Nakayama, R. Computer-Aided Diagnosis Scheme for Determining Histological Classification of Breast Lesions on Ultrasonographic Images Using Convolutional Neural Network. Diagnostics 2018, 8, 48.

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