RETRACTED ARTICLE: Computer aided diagnosis of brain tumor using novel classification techniques

Abstract

Brain cancer treatment mainly depends on the accurate detection of the tumor type, location, size and borders. Magnetic resonance images (MRI) can be used to analyze the properties of the desired region such as tissues and tumors with automated and semi-automated approaches. So, the extraction of MRI brain tumor image is a challenging task in medical image processing. The major problems associated with MRI analysis by a physician are time consuming and the accuracy depends on the expertise of the physician.This limitation can be overcome by the computer aided diagnosis (CAD) technology. In this paper, a CAD system is designed to detect brain tumors with computer assistance using T1 and T2 weighted MR images. The designed system classifies the tumor into benign or malignant from MR Image using a novel automated method which increases the performance and reduces the complexity involved in the tumor diagnosis. The CAD system has four stages such as image acquisition, segmentation, feature extraction and classification. Segmentation is done with the help of K-means clustering, which enhances the medical image and the clustering quality to avoid local optima and to find global optima. The feature extraction is performed by gray level co-occurrence matrix (GLCM). The tumor classification is done using support vector machine (SVM) and bag of visual words (BOVW) classifiers. The test result of the SVM classifier gives accuracy 95.0%, sensitivity 91.79% and specificity 94.75%. Whereas, the BOVW classifier yields results of accuracy 96.0%, sensitivity 90.0% and specificity 100%. This result shows that, the designed system classifies the tumor into benign or malignant with a good level of accuracy.