Combining optimal wavelet statistical texture and recurrent neural network for tumour detection and classification over MRI

Brain tumor is one of the major causes of death among other types of the cancer because brain is a very sensitive, complex and central part of the body. Proper and timely diagnosis can prevent the life of a person to some extent. Therefore, in this paper, an efficient brain tumor detection system is proposed using combining optimal wavelet statistical texture features and recurrent neural network (RNN). The proposed system consists of four phases namely; feature extraction feature selection, classification and segmentation. First, noise removal is performed as the preprocessing step on the brain MR images. After that, texture features (both the dominant run length and co-occurrence texture features) are extracted from these noise free MR images. The high number of features is reduced based on oppositional gravitational search algorithm (OGSA). Then, selected features are given to the Recurrent Neural Network (RNN) classifier to classify an image as normal or abnormal. After the classification process, abnormal images are given to the segmentation stage to segment the ROI region with the help of modified region growing algorithm (MRG). The performance of the proposed methodology is analyzed in terms of different metrics and experimental results are compared with existing methods.