Hybridized Deep Convolutional Neural Network and Fuzzy Support Vector Machines for Breast Cancer Detection
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Date
2021
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SN Computer Science
Abstract
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 (CADs) in screeening mammography can assist the radiologist in avoiding missing breast cancer cases. However, many existing systems are prone to false detection or misclassification and are majorly tailored towards either binary classification or theer-class classification. Therefore, this study seeks to develop both two-class and three-class models for breast cancer detection and classification employing a dep convolutional neural network (DCNN) with fuzzy support vector machines. The models were developed using mammograms downloaded from the digital databse for screening mammogragraphy (DDSM) and curated breast imaging subset CBISDDSM data repositories. The datasets were preprocessed, and feature extracted for classification with DCNN and fuzzy support vector machine (SVM). The system was evaluated using accuracy, sensitivity, AUC, Fi-score, and confusion matrix. The 3-class model gave an accuracy of 81.43% for 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.
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Keywords
Breast cancer, Convolutional Neural Network, Cancer detection, Deep learning, Fuzzy support vector