Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis

Shintaro Suzuki, Xiaoyong Zhang, Noriyasu Homma, Kei Ichiji, Norihiro Sugita, Yusuke Kawasumi, Tadashi Ishibashi, Makoto Yoshizawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Citations (Scopus)

Abstract

In recent years, a deep convolutional neural network (DCNN) has attracted great attention due to its outstanding performance in recognition of natural images. However, the DCNN performance for medical image recognition is still uncertain because collecting a large amount of training data is difficult. To solve the problem of the DCNN, we adopt a transfer learning strategy, and demonstrate feasibilities of the DCNN and of the transfer learning strategy for mass detection in mammographic images. We adopt a DCNN architecture that consists of 8 layers with weight, including 5 convolutional layers, and 3 fully-connected layers in this study. We first train the DCNN using about 1.2 million natural images for classification of 1,000 classes. Then, we modify the last fully-connected layer of the DCNN and subsequently train the DCNN using 1,656 regions of interest in mammographic image for two classes classification: mass and normal. The detection test is conducted on 198 mammographic images including 99 mass images and 99 normal images. The experimental results showed that the sensitivity of the mass detection was 89.9 % and the false positive was 19.2 %. These results demonstrated that the DCNN trained by transfer learning strategy has a potential to be a key system for mammographic mass detection computer-aided diagnosis (CAD). In addition, to the best of our knowledge, our study is the first demonstration of the DCNN for mammographic CAD application.

Original languageEnglish
Title of host publication2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1382-1386
Number of pages5
ISBN (Electronic)9784907764500
DOIs
Publication statusPublished - 2016 Nov 18
Event55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016 - Tsukuba, Japan
Duration: 2016 Sep 202016 Sep 23

Publication series

Name2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016

Other

Other55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016
CountryJapan
CityTsukuba
Period16/9/2016/9/23

Keywords

  • Computer-Aided Diagnosis/Detection
  • DCNN
  • Deep Learning
  • Medical and Welfare Systems
  • Neural Networks
  • Signal and/or Image Processing
  • Transfer Learning
  • mammogram

ASJC Scopus subject areas

  • Control and Optimization
  • Instrumentation
  • Control and Systems Engineering

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  • Cite this

    Suzuki, S., Zhang, X., Homma, N., Ichiji, K., Sugita, N., Kawasumi, Y., Ishibashi, T., & Yoshizawa, M. (2016). Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. In 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016 (pp. 1382-1386). [7749265] (2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SICE.2016.7749265