Performance Analysis of Neural Network and ANFIS in Brain MR Image Classification

In earlier days, the brain MR images classification and tumor detection was done by human inspection. But this classification method is impractical for large amounts of data and is also non-reproducible. MR images always contain a noise caused by operator performance which leads to serious inaccurate classification. Hence automated classification is preferred for accuracy. The use of artificial intelligence techniques, for instance, neural networks, fuzzy logic, neuro fuzzy have shown great improvement in this field. Hence, in this paper the ANFIS is applied for classification and detection purposes. Decision making was performed in two stages: feature extraction using the principal component analysis (PCA) and the ANFIS trained with the back propagation gradient descent method in combination with the least squares method. The performance of the ANFIS classifier is evaluated in terms of training performance and classification accuracies and the results will confirm that the proposed ANFIS classifier has potential in detecting the tumors.