Breast Tumors Classification Using Adaptive Neuro-Fuzzy Inference System

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Abstract

Breast cancer is one of the world's leading causes of cancer-related deaths and ranks second in the cancer fact sheets. In Sudan, the increasing incidence, detection at late stages, and early onset of the disease make early detection and diagnosis of breast cancer an overbearing task. The objective of this study was to create a computer interfacing system for the localization, detection, and classification of breast masses using adaptive neuro-fuzzy inference system (ANFIS). The ANFIS classifier was used to detect the breast cancer when 5 features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy-logic qualitative approach. Results demonstrated that the proposed methodologies have high potential in enhancing breast images and localizing, detecting, and classifying the breast tumor. The system was able to achieve an accuracy of 94.4% sensitivity, 100% specificity, 97.1% positive predictive value, 100% negative predictive value, an Az value of .972, and an overall classification accuracy of 98%.

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