An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

Abstract

AbstractAn adaptive neuro‐fuzzy inference system is presented which is optimized by a genetic algorithm to classify normal and abnormal brain tumours. The classifier is fast and simple, named genetic algorithm‐adaptive neuro‐fuzzy inference system, and the determined learning rules minimize its error and improve its accuracy. The presented system follows five steps including preprocessing, morphological operation, feature extraction, feature selection, and classification. Morphological operators segment the abnormal regions and calculate the tumour area. The statistical features and the grey‐level co‐occurrence matrix are employed for feature extraction. Magnetic resonance images are considered and 12 statistical features are extracted, then the genetic algorithm‐based selection technique helps to select features and reduce the extracted features and improves the accuracy and decision time. So, the high dimensionality and the computational complexity of the adaptive neuro‐fuzzy inference system are reduced, and the classifier decides more efficiently. The input data are the figshare brain tumour dataset with 670 abnormal and 670 normal magnetic resonance images, and the classifier requires 10.788 s for classification. The efficient performance of the genetic algorithm‐adaptive neuro‐fuzzy inference system is confirmed by the accuracy of 99.85%, sensitivity of 99.7%, specificity of 100%, precision of 100%, and mean square error of 0.0027.