Medical Images Classification by using Artificial Intelligence Techniques

Medical Images Classification by using Artificial Intelligence Techniques
Authors:WESSAM LAHMOD NADOS, R. DILEEP KUMAR

Abstract: As known, the brain tumor is the most common fatality in the current scenario of health care society. Hence, a robust and more accurate detection system provides an efficient and fast way for diagnosis of the brain tumor is highly essential for treatment planning which can minimize the fatal results. Accurate results can be obtained only through computer aided automated systems to avoid the human error in manual interpretation of medical image content and to obtain high performance and efficiency. The proposed method consists of many stages, namely, image acquisition, image segmentation, features extraction, and classification. Modified K-means clustering algorithm that named weighted K-means used in segmentation stage to convert images into set of regions, the output of this stage will be input to features extraction stage. In features extraction stage multi extracted textural features using Gray Level Co-occurrence Matrix (GLCM) matrices in four directions, these features are used in classification stage. K-Nearest Neighbor classifier is used in decision making. In the experiment we test many MRI images and the results show that provides better output and the classification accuracy of our method is 86%. 

Keywords: Brain Tumor, A.I Techniques; MRI; Weighted K-Means, GLCM, K-Nearest.

I. INTRODUCTION 

 The brain tumor is one of the major causes for the increase in mortality among children and adults. A tumor is any mass that results from abnormal growths of cells in the brain. It may affect any person at almost any age. Brain tumor effects may not be the same for each person. Tumors can directly destroy healthy brain cells[2]. They can also indirectly damage healthy cells by crowding other parts of the brain and causing inflammation, brain swelling, and pressure within the skull [1].Brain tumors are either malignant or benign.

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