Employing a Hybrid Technique to Detect Tumor in Medical Images

Abstract

In this article, a hybrid approach using watershed, genetic, and support vector machine algorithms is presented to detect brain tumors in medical images. Employing this method, the images are segmented properly and the brain tumor is detected with high accuracy. Accordingly, first, grayscale and median filters are used to pre-process the images for noise removal. Then, the watershed algorithm is applied for segmentation of the image and then with using genetic features are explored. Finally, the SVM algorithm is applied to learn extracted features and diagnose brain tumors with high accuracy. Considering the accuracy, precision, and recall, the evaluation results indicate that the proposed method can segment and classify the images well, and it outperforms conventional algorithms with an accuracy of 95% and precision of 97%.