Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm

Gaber, T ORCID: https://orcid.org/0000-0003-4065-4191, Ismail, G, Anter, A, Soliman, M, Ali, M, Semary, N, Hassanien, AE and Snasel, V 2015, Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm , in: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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Abstract

The early detection of breast cancer makes many women survive. In this paper, a CAD system classifying breast cancer thermograms to normal and abnormal is proposed. This approach consists of two main phases: automatic segmentation and classification. For the former phase, an improved segmentation approach based on both Neutrosophic sets (NS) and optimized Fast Fuzzy c-mean (F-FCM) algorithm was proposed. Also, post-segmentation process was suggested to segment breast parenchyma (i.e. ROI) from thermogram images. For the classification, different kernel functions of the Support Vector Machine (SVM) were used to classify breast parenchyma into normal or abnormal cases. Using benchmark database, the proposed CAD system was evaluated based on precision, recall, and accuracy as well as a comparison with related work. The experimental results showed that our system would be a very promising step toward automatic diagnosis of breast cancer using thermograms as the accuracy reached 100%.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher: IEEE
Related URLs:
Depositing User: Dr. Tarek Gaber
Date Deposited: 11 Sep 2019 10:57
Last Modified: 11 Sep 2019 11:00
URI: http://usir.salford.ac.uk/id/eprint/52104

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