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Sebri, A., Malek, J., and Tourki, R., (2007) Automated breast cancer diagonosis based on GVF-snake segmentation, wavelet features extraction and neural network classification, J. Com-puter Science, 3(8), 600–607.

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• TITLE: CANFIS—a computer aided diagnostic tool for cancer detection

  AUTHORS: Latha Parthiban, R. Subramanian

  KEYWORDS: Neural Network; Coactive Neuro-Fuzzy Inference Systems; Probabilistic Neural Network; Principal Component Analysis; Stern Series; Wisconsin Breast Cancer Data

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.2 No.5, September 17, 2009

  ABSTRACT: In this investigation, an approach using Coac-tive Neuro-Fuzzy Inference System (CANFIS) as diagnosis system for breast cancer has been proposed on Wisconsin Breast Cancer Data (WBCD). It is occasionally difficult to attain the ultimate diagnosis even for medical experts due to the complexity and non-linearity of the rela-tionships between the large measured factors, which can be possibly resolved with a human like decision-making process using Artificial Intelligence (AI) algorithms. CANFIS is an AI algorithm which has the advantages of both fuzzy inference system and neural networks and can deal with ambiguous data and learn from the past data by itself. The Multi Layer Percep-tron Neural Network (MLPNN), Probabilistic Neural Network (PNN) Principal Component Analysis (PCA), Support Vector Machine (SVM) and Self Organizing Map (SOM) were also tested and benchmarked for their p