Bandlet transform based brain tumor detection and classification of Magnetic resonance image using Coactive Neuro Fuzzy Inference System in comparison with Adaptive Neuro Fuzzy Inference System classifier

Abstract

The aim of study is comparative analysis of two algorithms co-adaptive neuro fuzzy inference system classifiers for better efficiency with adaptive neuro fuzzy inference system for brain tumor detection. Materials and Methods: The data set used for this experiment is taken from Kaggle open access dataset. A total of 20 brain magnetic resonance images are used forco-adaptive neuro fuzzy inference system (Group 1) it is compared with adaptive neuro fuzzy inference system (Group 2). To measure the accuracy 80% of the images are used for training, 10% for testing and 10% for validation. Threshold 0.05 and g-power is 80. The performance analysis is done to validate the better methodology in the SPSS Tool. Result: The initial research using adaptive neuro fuzzy inference system(ANFIS) in detection of brain tumor disease has achieved accuracy of 93% and the proposed system has attained accuracy of 96%. Conclusion: It is concluded that the detection of innovative brain tumor in this view, the diagnosis of brain tumor disease using co-adaptive neuro fuzzy inference system (CANFIS) appears to be with better results compared to adaptive neuro fuzzy inference system (ANFIS).