Abstract:

Breast cancer is one of the leading causes of women death worldwide currently. Developing a computer-aided diagnosis system for breast cancer detection became an interesting problem for many researchers in recent years. Researchers focused on deep learning techniques for classification problems, including Convolutional Neural Networks (CNNs), which achieved great success. CNN is a particular type of deep, feedforward network that has gained attention from the research community and achieved great successes, especially in biomedical image processing. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to classify breast cancer histopathological images from the publically available (BreakHis dataset). The data set includes both benign and malignant images with four different magnification factors. A patch strategy method proposed based on the extraction of image patches for training the CNN and the combination of these patches for final classification. AlexNet model is considered in this work with patch strategy, and pre-trained AlexNet is used for further fine-tuning. The obtained features are then classified by using support vector machines (SVM). The evaluation results show that the pre-trained Alexnet with SVM classification and patch strategy yields the best accuracy. Accuracy between 92% and 96% was achieved using five-fold cross-validation technique for different magnification factors.

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