Performance Analysis of Brain Tumor Detection based on Fuzzy Logic and Neural Network Classifier - Anbumozhi, Selladurai (Current Medical Imaging, 2016)
In medical image processing, image fusion technique is used to enhance the brain tumors or inertial component of the brain for better medical diagnosis and further clinical treatment. In this paper, the brain tumor is detected and diagnosed by the following stages; preprocessing, fuzzy logic based fusion, feature extraction, Genetic algorithm and classification. Mamdani Fuzzy rules are constructed and used for brain tumor enhancement. Local binary and ternary pattern are extracted from the fused image and best features are selected by genetic algorithm. The extracted features are trained and classified into normal or abnormal brain image by feed forward back propagation neural networks. Morphological operations are used to segment the brain tumor from the classified brain image. The methodology presented in this paper is tested over the images available from the public datasets. The proposed system achieved the sensitivity rate of 99.67%, specificity rate of 99.56% and accuracy of 98.75%.
Keywords: Cerebral MRI images