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

Gaber, T; Ismail, G; Anter, A; Soliman, M; Ali, M; Semary, N; Hassanien, AE; Snasel, V

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%.

Citation

Gaber, T., Ismail, G., Anter, A., Soliman, M., Ali, M., Semary, N., …Snasel, V. Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm. Presented at 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

• Presentation Conference Type: Other
• Conference Name: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
• Publication Date: Aug 5, 2015
• Deposit Date: Sep 11, 2019
• DOI: https://doi.org/10.1109/EMBC.2015.7319334
• Publisher URL: http://dx.doi.org/10.1109/EMBC.2015.7319334
• Related Public URLs: https://ieeexplore.ieee.org/xpl/conhome/7302811/proceeding
• Additional Information: Event Type : Conference