AlexResNet+: A Deep Hybrid Featured Machine Learning Model for Breast Cancer Tissue Classification

Abstract

The exponential rise in cancer diseases, primarily the breast cancer has alarmed academia-industry to achieve more efficient and reliable breast cancer tissue identification and classification. Unlike classical machine learning approaches which merely focus on enhancing classification efficiency, in this paper the emphasis was made on extracting multiple deep features towards breast cancer diagnosis. To achieve it, in this paper A Deep Hybrid Featured Machine Learning Model for Breast Cancer Tissue Classification named, AlexResNet+ was developed. We used two well known and most efficient deep learning models, AlexNet and shorted ResNet50 deep learning concepts for deep feature extraction. To retain high dimensional deep features while retaining optimal computational efficiency, we applied AlexNet with five convolutional layers, and three fully connected layers, while ResNet50 was applied with modified layered architectures. Retrieving the distinct deep features from AlexNet and ResNet deep learning models, we obtained the amalgamated feature set which were applied as input for support vector machine with radial basis function (SVM-RBF) for two-class classification. To assess efficacy of the different feature set, performances were obtained for AlexNet, shorted ResNet50 and hybrid features distinctly. The simulation results over DDMS mammogram breast cancer tissue images revealed that the proposed hybrid deep features (AlexResNet+) based model exhibits the highest classification accuracy of 95.87%, precision 0.9760, sensitivity 1.0, specificity 0.9621, F-Measure 0.9878 and AUC of 0.960.