Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks

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  • Miao Wu
    College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China
  • Chuanbo Yan
    College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China
  • Huiqiang Liu
    College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China
  • Qian Liu
    Graduate College, Xinjiang Medical University, Urumqi 830011, China

Description

<jats:p>Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.</jats:p>

Journal

  • Bioscience Reports

    Bioscience Reports 38 (3), BSR20180289-, 2018-05-08

    Portland Press Ltd.

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