Hybridized Deep Convolutional Neural Network and Fuzzy Support Vector Machines for Breast Cancer Detection

A cancerous development that originates from breast tissue is known as breast cancer, and it is reported to be the leading cause of women death globally. Previous researches have proved that the application of Computer-Aided Detection (CADe) in screening mammography can assist the radiologist in avoiding missing breast cancer cases. However, many of the existing systems are prone to false detections or misclassifications and are majorly tailored towards either binary classification or three-class classification. Therefore, this study seeks to develop both two-class and three-class models for breast cancer detection and classification employing a deep convolutional neural network (DCNN) with fuzzy support vector machines. The models were developed using mammograms downloaded from the digital database for screening mammography (DDSM) and curated breast imaging subset CBISDDSM data repositories. The datasets were pre-processed, and features extracted for classification with DCNN and fuzzy support vector machines (SVM). The system was evaluated using accuracy, sensitivity, AUC, F1-score, and confusion matrix. The 3-class model gave an accuracy of 81.43% for the DCNN and 85.00% accuracy for the fuzzy SVM. The first layer of the serial 2-layer DCNN with fuzzy SVM for binary prediction yielded 99.61% and 100.00% accuracy, respectively. However, the second layer gave 86.60% and 91.65%, respectively. This study’s contribution to knowledge includes the hybridization of deep convolutional neural network with fuzzy support vector machines to improve the detection and classification of cancerous and non-cancerous breast tumours in both binary classification and three-class classification scenarios.