West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter. Asian Pacific Journal of Cancer Prevention 1513-7368 19 10 2018 10 01 An Efficient Method for Brain Tumor Detection Using Texture Features and SVM Classifier in MR Images 2789 2794 69798 10.22034/APJCP.2018.19.10.2789 EN Kavin Kumar K Department of Electronics and communication Engineering, Kongu Engineering College, Perundurai, Erode -638 060, Tamil Nadu, India. Meera Devi T Department of Electronics and communication Engineering, Kongu Engineering College, Perundurai, Erode -638 060, Tamil Nadu, India. Maheswaran S Department of Electronics and communication Engineering, Kongu Engineering College, Perundurai, Erode -638 060, Tamil Nadu, India. Journal Article 2017 12 23 Objective: Detection and classification of abnormalities in Magnetic Resonance (MR) brain images in medical field<br />is very much needed. The proposed brain tumor classification system composed of denoising, feature extraction and<br />classification. Noise is one of the major problems in the medical image and due to that retrieval of useful information<br />from the image is difficult. The proposed method for denoising an image is PURE-LET transform. Methods: This<br />method preserves the diagnostic property of the images. In feature extraction, combination of Modified Multi-Texton<br />Histogram (MMTH) and Multi-Texton Microstructure Descriptor (MTMD) is used and then Gray Level Co-occurrence<br />Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM)are used to extract the feature from the image to<br />compare performance. In classification, classifiers like Support Vector Machine (SVM), K Nearest Neighbors (KNN)<br />and Extreme Learning Machine (ELM)are trained by the extracted features and are used to classify the images. Result:<br />The performance of feature extraction methods with three different classifiers are compared in terms of the performance<br />metrics like sensitivity, specificity, and accuracy. Conclusion: The result shows that the combination of MMTH and<br />MTMD with SVM shows the highest accuracy of 95%. https://journal.waocp.org/article_69798_9a9e7d28160b324a63df709626b2b4cc.pdf