www.astesj.com 40 An Advanced Algorithm Combining SVM and ANN Classifiers to Categorize Tumor with Position from Brain MRI Images Rasel Ahmmed *,1 , Md. Asadur Rahman 2 , Md. Foisal Hossain 3 1 Department of Electronics and Communication Engineering, East West University, Dhaka, Bangladesh 2 Department of Biomedical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh 3 Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh A R T I C L E I N F O A B S T R A C T Article history: Received: 30 November, 2017 Accepted: 05 February, 2018 Online: 08 March, 2018 Brain tumor is such an abnormality of brain tissue that causes brain hemorrhage. Therefore, apposite detections of brain tumor, its size, and position are the foremost condition for the remedy. To obtain better performance in brain tumor and its stages detection as well as its position in MRI images, this research work proposes an advanced hybrid algorithm combining statistical procedures and machine learning based system Support Vector Machine (SVM) and Artificial Neural Network (ANN). This proposal is initiated with the enhancement of the brain MRI images which are obtained from oncology department of University of Maryland Medical Center. An improved version of conventional K-means with Fuzzy C-means algorithm and temper based K-means & modified Fuzzy C-means (TKFCM) clustering are used to segment the MRI images. The value of K in the proposed method is more than the conventional K-means. Automatically updated membership of FCM eradicates the contouring problem in detection of tumor region. The set of statistical features obtained from the segmented images are used to detect and isolate tumor from normal brain MRI images by SVM. There is a second set of region based features extracted from segmented images those are used to classify the tumors into benign and four stages of the malignant tumor by ANN. Besides, the classified tumor images provide a feature like orientation that ensures exact tumor position in brain lobe. The classifying accuracy of the proposed method is up to 97.37% with Bit Error Rate (BER) of 0.0294 within 2 minutes which proves the proposal better than the others. Keywords: Magnetic resonance imaging (MRI) Temper based K-means & modified fuzzy C-means clustering (TKFCM) Artificial Neural Network (ANN) Support Vector Machine (SVM) Tumor Size; and Tumor Position 1. Introduction The brain controls all psychological and physiological activities of human body. These functional activities can be disrupted or damaged due to the abnormal cell division or growing tumor in our brain that causes miscellaneous problems to the malfunction of our body. A human brain is divided into several major areas and these major areas (see Fig. 1) are related to different functional part of our body. Especially, these major areas are known as frontal lobe (marked with 1), central lobe (marked by 2, 3, & 4), parietal lobe (marked by 5 & 6), occipital lobe (marked with 7), and temporal lobe (marked with 8). Frontal lobe functions to control our thinking, emotion, innovation, and other cognitive works. Central lobe is a part of the frontal lobe and this part controls our movement related functions. The temporal lobe is responsible for listening and it helps to avoid the uncertainty principle. Occipital lobe helps us to see or observe anything. Another major part that controls the speech processing through reading compression area, sensory speech area and motor speech area of Broca is parietal lobe. These major areas can be affected by tumors which are definitely threat to our normal living. Therefore, the proper detection is the first priority for the remedy. This paper presents an efficient method for the detection of the tumor size as well as the position with classified tumor stages from MRI images and this work is an extension of our conference paper [1] that was presented in ECCE-2017. ASTESJ ISSN: 2415-6698 * Rasel Ahmmed, East West University, Email: rsa@ewubd.edu Advances in Science, Technology and Engineering Systems Journal Vol. 3, No. 2, 40-48 (2018) www.astesj.com Special issue on Advancement in Engineering Technology https://dx.doi.org/10.25046/aj030205