Breast Cancer Classification using Customized ResNet based Convolution Neural Networks

Abstract

Deep learning is the most frequently used tool in the classification of tumors in medical applications. In recent decades, many research works have been done on the Breast Imaging Reporting & Data System (BI-RADS) atlas based classification of Breast cancer. As reported in the existing research works, training the larger datasets is a challenging task. Therefore, a customized ResNet based Convolution Neural Network (cRN-CNN) with batch normalization is proposed in this manuscript for addressing the above mentioned issue. The proposed cRN-CNN method has the advantage of faster training and computationally effective for the classification of BIRADS atlas based MRI breast cancer records, where the proposed model's performance is superior compared to the conventional CNN model. The extensive experiments performed on the Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) dataset confirmed that the proposed cRN-CNN method achieved better classification results than the existing methods. In the proposed model, the deformation technique based on elastic deformation is also applied to increase the training size of data that helps to improve the outcomes of prediction up-to 99.80%, because of the efficient strategy of batch normalization as customization and elastic deformation.