title: Thermogram breast cancer prediction approach based on Neutrosophic sets and fuzzy c-means algorithm.
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%.
authors.forename: Tarek
authors.initials: T
authors.lastname: Gaber
authors.forename: Gehad
authors.initials: G
authors.lastname: Ismail
authors.forename: Ahmed
authors.initials: A
authors.lastname: Anter
authors.forename: Mona
authors.initials: M
authors.lastname: Soliman
authors.forename: Mona
authors.initials: M
authors.lastname: Ali
authors.forename: Noura
authors.initials: N
authors.lastname: Semary
authors.forename: Aboul Ella
authors.initials: AE
authors.lastname: Hassanien
authors.forename: Vaclav
authors.initials: V
authors.lastname: Snasel
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms; D016208:Databases, Factual; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D060388:Support Vector Machine