A Hybrid CNN and RBF-Based SVM Approach for Breast Cancer Classification in Mammograms

Abstract

One of the most powerful ideas in deep learning is the transfer learning technique. Transfer learning can be utilized to take a knowledge from what a deep neural network has learned from a particular task and apply that knowledge to a different task. Transfer learning is very useful when the size of the training samples of interest is small to train a neural network from scratch. This research focuses on the concept of transfer learning where the Convolutional Neural Network (CNN) power can be utilized as a features extractor to help with classifying benign from malignant breast cancer images. In addition, Support Vector Machine (SVM) classifier based on Radial Basis Function (RBF) was adapted for its flexibility in fitting the data dimension space adequately by tuning the kernel width. The hybridization between CNN and RBF-Based SVM showed robust results for both the dataset and the application task of this research. The contribution of this paper can be summarized in three major parts. First, a CNN was implemented from scratch on a large number of available spine images to classify images of two different spine views (sagittal and axial) in order to transfer the learning process to the CNN of breast images. Second, retrain the spine CNN on the images of breast cancer to classify between benign and malignant cases by fine-tuning. Finally, the features were extracted from the retrained CNN and fed to RBF-Based SVM to classify benign from malignant breast mammogram images.

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