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

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dc.contributor.authorOlowookere, Toluwase Ayobami
dc.date.accessioned2022-03-02T11:43:26Z
dc.date.available2022-03-02T11:43:26Z
dc.date.issued2022
dc.description.abstractA 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.en_US
dc.identifier.urihttp://dspace.run.edu.ng:8080/jspui/handle/123456789/1771
dc.language.isoenen_US
dc.publisherSpringer Nature Computer Scienceen_US
dc.subjectBreast canceren_US
dc.subjectConvolutional neural networken_US
dc.subjectCancer detectionen_US
dc.subjectDeep learningen_US
dc.subjectFuzzy support vector Machinesen_US
dc.titleHybridized Deep Convolutional Neural Network and Fuzzy Support Vector Machines for Breast Cancer Detectionen_US
dc.typeArticleen_US
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