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Breast Mass Classification using the Fundamental Deep Learning Approach: To build the optimal model applying various methods that influence the performance of CNN

  • Lee, Jin (Bachelor of Arts degree in biology from Taylor University) ;
  • Choi, Kwang Jong (Korea Christian International School) ;
  • Kim, Seong Jung (Biomedical Engineering Branch, Division of Precision Medicine and Cancer Informatics, National Cancer Center) ;
  • Oh, Ji Eun (Biomedical Engineering Branch, Division of Precision Medicine and Cancer Informatics, National Cancer Center) ;
  • Yoon, Woong Bae (Biomedical Engineering Branch, Division of Precision Medicine and Cancer Informatics, National Cancer Center) ;
  • Kim, Kwang Gi (Biomedical Engineering Branch, Division of Precision Medicine and Cancer Informatics, National Cancer Center)
  • Received : 2016.09.01
  • Accepted : 2016.10.10
  • Published : 2016.09.30
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Abstract

Deep learning enables machines to have perception and can potentially outperform humans in the medical field. It can save a lot of time and reduce human error by detecting certain patterns from medical images without being trained. The main goal of this paper is to build the optimal model for breast mass classification by applying various methods that influence the performance of Convolutional Neural Network (CNN). Google's newly developed software library Tensorflow was used to build CNN and the mammogram dataset used in this study was obtained from 340 breast cancer cases. The best classification performance we achieved was an accuracy of 0.887, sensitivity of 0.903, and specificity of 0.869 for normal tissue versus malignant mass classification with augmented data, more convolutional filters, and ADAM optimizer. A limitation of this method, however, was that it only considered malignant masses which are relatively easier to classify than benign masses. Therefore, further studies are required in order to properly classify any given data for medical uses.

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References

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