In the present study, the effectiveness of the adaptive resonance theory neural network (ART2) is illustrated in the context of automatic classification of abnormal brain tumor images. Abnormal images from four different classes namely metastase, meningioma, glioma and astrocytoma have been used in this work. Initially, textural features are extracted from these images. An extensive feature selection is performed to optimize the number of features. These optimized features are then used to classify the images using ART2 neural network. Experimental results show promising results for the ART2 network in terms of classification accuracy and convergence rate. A comparison is made with other conventional classifiers to show the superior nature of ART2 neural network. The classification accuracy of the ART2 classifier is significantly higher than the statistical classifiers. ART2 classifier is also computationally feasible over other neural classifiers. Thus this work suggests ART2 neural network as an optimal image classifier which finds application in clinical field.
