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Article

Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy

1
Department of Mathematics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah 711103, India
2
Institute of Research and Development of Processes, University of the Basque Country, 48940 Leioa, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Shengkun Xie
Received: 29 March 2021 / Revised: 7 May 2021 / Accepted: 14 May 2021 / Published: 18 May 2021
Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software. View Full-Text
Keywords: breast cancer; mammography; classification; multi-model ensemble; Fuzzy c-means breast cancer; mammography; classification; multi-model ensemble; Fuzzy c-means
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MDPI and ACS Style

Ghosh, S.; Samanta, G.; De la Sen, M. Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy. Computation 2021, 9, 59. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9050059

AMA Style

Ghosh S, Samanta G, De la Sen M. Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy. Computation. 2021; 9(5):59. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9050059

Chicago/Turabian Style

Ghosh, Sarada, Guruprasad Samanta, and Manuel De la Sen. 2021. "Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy" Computation 9, no. 5: 59. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9050059

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