Abstract
The brain magnetic resonance imaging (MRI), analysis and segmentation plays one of the crucial roles in medical diagnosis and facilitates in an early detection of diseases in critical medical conditions, Due to the structural complexity and type of the tumor, radiologists are facing difficulties in extracting essential features of the image which are crucial in treating the patient. Therefore, correct, and meaningful segmentation of brain MRI is a challenging task and is required for further processing. This article proposes machine learning based automatic brain MRI segmentation and classification. The pre-processing step is the vital part of the algorithm, where the discrete wavelet transforms (DWT) and median filtering help in identifying and pointing the exact location of the tumor. The preprocessed image is further segmented by an improved original Fuzzy C-means (FCM) clustering technique. The feature extraction and classification is performed by support vector machine (SVM) classifier. It is found that the simulation associated with ground truth data provides better segmentation results in terms of accuracy, sensitivity, and dice coefficient.
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