Affiliations: School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, China
Correspondence:
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Corresponding author: Yubao Hou, School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, China. E-mail: [email protected].
Abstract: The automatic classification of breast cancer pathological images has important clinical application value. However, to develop the classification algorithm using the artificially extracted image features faces several challenges including the requirement of professional domain knowledge to extract and compute highiquality image features, which are often time-consuming, laborious, and difficult. For overcoming these challenges, this study developed and applied an improved deep convolutional neural network model to perform automatic classification of breast cancer using pathological images. Specifically, in this study, data enhancement and migration learning methods are used to effectively avoid the overfitting problems with deep learning models when they are limited by training image sample size. Experimental results show that a 91% recognition rate or accuracy when applying this improved deep learning model to a publicly available dataset of BreaKHis. Comparing with other previously used models, the new model yields good robustness and generalization.
Keywords: Breast cancer histopathological image classification, deep leaning, convolutional neural network, transfer learning, data augmentation, open dataset of BreaKHis
