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

Content Provider	IEEE Xplore Digital Library
Author	Gaber, T. Ismail, G. Anter, A. Soliman, M. Ali, M. Semary, N. Hassanien, A.E. Snasel, V.
Copyright Year	2015
Description	Author affiliation: IT4Innovation, VSB-TUO, Czech Republic (Snasel, V.) \|\| Suez Canal Univ., Suez, Egypt (Gaber, T.) \|\| Beni-Suef Univ., Beni-Suef, Egypt (Anter, A.) \|\| Cairo Univ., Cairo, Egypt (Ismail, G.; Soliman, M.; Hassanien, A.E.) \|\| Minia Univ., Minia, Egypt (Ali, M.) \|\| Menoufia Univ., Shebin El-Kom, Egypt (Semary, N.)
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%.
Starting Page	4254
Ending Page	4257
File Size	1542121
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319334
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Feature extraction Breast cancer Support vector machines Level set Design automation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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