A Survey of Brain Tumor Segmentation and Classification Algorithms
- PMID:34564105
- PMCID: PMC8465364
- DOI: 10.3390/jimaging7090179
A Survey of Brain Tumor Segmentation and Classification Algorithms
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.
Keywords: brain tumor; classification; deep learning; region growing; segmentation; shallow machine learning.
Conflict of interest statement
The authors declare no conflict of interest.
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References
- Afework Y.K., Debelee T.G. Detection of Bacterial Wilt on Enset Crop Using Deep Learning Approach. Int. J. Eng. Res. Afr. 2020;51:131–146. doi: 10.4028/www.scientific.net/JERA.51.131. - DOI
- Debelee T.G., Schwenker F., Ibenthal A., Yohannes D. Survey of deep learning in breast cancer image analysis. Evol. Syst. 2019;11:143–163. doi: 10.1007/s12530-019-09297-2. - DOI
- Debelee T.G., Amirian M., Ibenthal A., Palm G., Schwenker F. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing; Berlin/Heidelberg, Germany: 2018. Classification of Mammograms Using Convolutional Neural Network Based Feature Extraction; pp. 89–98. - DOI
- Debelee T.G., Gebreselasie A., Schwenker F., Amirian M., Yohannes D. Classification of Mammograms Using Texture and CNN Based Extracted Features. J. Biomimetics Biomater. Biomed. Eng. 2019;42:79–97. doi: 10.4028/www.scientific.net/JBBBE.42.79. - DOI
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