Authors : Akila and Uma Maheswari
Abstract: This study aims to reveal a comparative analysis of classifier performance on medical image diagnosis, particularly for brain tumor detection and classification. The detection of brain tumor stands in need of Magnetic Resonance Imaging (MRI). The moment invariant feature extraction has been evaluated to categorize the MRI Slices as Normal, Benign and Malignant by Neural Network Classifier. In the comparative study, researchers examine the precision rate of aforementioned classification with extracted features and the classification of brain images with selective features by association rule based neural network classifier. The results are then analyzed with Receiver Operating Characteristics (ROC) curve and compared to illustrate the method producing higher accuracy rate in tumor recognition. Factually, the analysis proves that the classifier research under feature extraction followed by rule pruning method affords better accuracy rate.
Akila and Uma Maheswari , 2013. Comparative Analysis of Classifier Performance on Medical Image Diagnosis. International Journal of Soft Computing, 8: 199-206.