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An Enhanced Medical Diagnosis Sustainable System for Brain Tumor Detection and Segmentation using ANFIS Classifier

[ Vol. 14 , Issue. 2 ]


S. Kumarganesh* and M. Suganthi   Pages 271 - 279 ( 9 )


Background: Medical imaging plays a key role in detecting and diagnosing abnormal patterns from scanned images. The computer aided automatic detection of the brain tumor was proposed in this work using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier.

Methods: The proposed system has the following stages as noise reduction, Gabor transform, feature extraction and ANFIS classifier. The impulse noises in the brain images were detected and removed using directional filtering algorithm. Gabor transform transformed the spatial domain image into multi resolution image and further Pixel invariant, Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT) features were extracted from the Gabor transformed image and these features were given to the ANFIS classifier to classify the image as either normal and abnormal.

Discussion: The morphological operations were then applied over the abnormal image to segment the tumor regions.

Conclusion: The proposed system achieved 99.8%sensitivity, 99.7%specificity, and 99.8% accuracy for the brain tumor detection.


Brain tumor, impulse noise, features, classifiers, brain tissues, Local Binary Pattern (LBP).


Department of Electronics & Communication Engineering, Paavai Engineering College, Namakkai, Tamil Nadu, Department of Electronics & Communication Engineering, Mahendra College of Engineering, Namakkai, Tamil Nadu

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