Application of adaptive neuro-fuzzy inference systems for MR image classification and tumour detection

In earlier days, Magnetic-Resonance (MR) brain image classification and tumour detection was done by humans. But, this classification is impractical for large amounts of data. The uses of intelligence techniques have shown great improvement. Hence, in this paper the ANFIS is applied for classification and detection. Decision making was performed in two stages: feature extraction using Principal Component Analysis (PCA) and ANFIS trained with the backpropagation 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 confirms that the proposed ANFIS has potential in detecting the tumours.