Recognition of Brain Tumors Using Radon Transform and Gray Level Co-Occurrence Matrix Algorithm (Al-Mansour Journal, 2022)
The field of recognizing brain tumors in recent years has received wide attention by researchers due to the rapid spread of the disease. In this paper, brain tumors (normal, glioma and meningioma) were recognized by the proposed networks (Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLPNN) and Radial Basis Function neural network (RBFNN)). The dataset is divided into two parts: 450 MRI images for the training phase and 240 for the testing phase. Pre-processing was applied to the input images by converting the images to grayscale and removing noise on them by median filter. Then, the texture features of MRI images were extracted using Gray Level Co-Occurrence Matrix algorithm after radon transformation. The extracted texture features were passed to the proposed networks for classification and recognition of tumors. The technique of extracting brain tumors from the magnetic resonance image was added by segmentation algorithm and morphology operations on the images. The performance of (SVM, MLPNN and RBFNN) was measured based on sensitivity, specificity, and accuracy and the best results obtained by SVM with a quadratic kernel function were 98.45, 98.97, and 98.71 for sensitivity, specificity, and accuracy, respectively.
Keywords: multilayer perceptron neural network; Support vector machine; radial basis function neural network; gray level co-occurrence matrix; Radon transform; magnetic resonance imaging.