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• W3198503790 endingPage "179" @default.
• W3198503790 startingPage "179" @default.
• W3198503790 abstract "A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models' performance evaluation metrics." @default.
• W3198503790 created "2021-09-13" @default.
• W3198503790 creator A5005289592 @default.
• W3198503790 creator A5019625671 @default.
• W3198503790 creator A5049280353 @default.
• W3198503790 creator A5078096723 @default.
• W3198503790 date "2021-09-06" @default.
• W3198503790 modified "2023-10-17" @default.
• W3198503790 title "A Survey of Brain Tumor Segmentation and Classification Algorithms" @default.
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