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Distinction Between Benign and Malignant Breast Masses at Breast Ultrasound Using Deep Learning Method with Convolutional Neural Network

Overview
Journal Jpn J Radiol
Publisher Springer
Specialty Radiology
Date 2019 Mar 20
PMID 30888570
Citations 72
Authors
Tomoyuki Fujioka
Kazunori Kubota
Mio Mori
Yuka Kikuchi
Leona Katsuta
Mai Kasahara
Goshi Oda
Toshiyuki Ishiba
Tsuyoshi Nakagawa
Ukihide Tateishi
Affiliations
Soon will be listed here.
Abstract

Purpose: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound.

Materials And Methods: We retrospectively gathered 480 images of 96 benign masses and 467 images of 144 malignant masses for training data. Deep learning model was constructed using CNN architecture GoogLeNet and analyzed test data: 48 benign masses, 72 malignant masses. Three radiologists interpreted these test data. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.

Results: The CNN model and radiologists had a sensitivity of 0.958 and 0.583-0.917, specificity of 0.925 and 0.604-0.771, and accuracy of 0.925 and 0.658-0.792, respectively. The CNN model had equal or better diagnostic performance compared to radiologists (AUC = 0.913 and 0.728-0.845, p = 0.01-0.14).

Conclusion: Deep learning with CNN shows high diagnostic performance to discriminate between benign and malignant breast masses on ultrasound.

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