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Review of Automated Computerized Methods for Brain Tumor Segmentation and Classification

Author(s):

Umaira Nazar, Muhammad Attique Khan, Ikram Ullah Lali, Hong Lin*, Hashim Ali, Imran Ashraf and Junaid Tariq   Pages 1 - 13 ( 13 )

Abstract:


Recently, medical imaging and machine learning gained significant attention in the early detection of brain tumor. Compound structure and tumor variations, such as change of size, make brain tumor segmentation and classification a challenging task. In this review, we survey existing work on brain tumor, their stages, survival rate of patients after each stage, and computerized diagnosis methods. We discuss existing image processing techniques with a special focus on preprocessing techniques and their importance for tumor enhancement, tumor segmentation, feature extraction and features reduction techniques. We also provide the corresponding mathematical modeling, classification, performance matrices, and finally important datasets. Last but not least, a detailed analysis of existing techniques is provided which is followed by future directions in this domain.

Keywords:

Brain tumor, Preprocessing, Tumor segmentation, Feature extraction, Classification, Future trends.

Affiliation:

Department of CS, University of Sargodha, Department of Computer Science and Engineering, HITEC University Taxila, Department of Computer Science, University of Gujrat, Department of Computer and Mathematical Sciences, University of Houston – Downtown, Houston, TX, Department of Computer Science and Engineering, HITEC University Taxila, Department of Computer Science and Engineering, HITEC University Taxila, Department of Computer Science and Engineering, HITEC University Taxila



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