Abstract
Recent medical imaging research faces the challenges of detecting brain tumor at earlier stage despite having promising techniques like MRI, X-ray and CT-Scan. Medical image processing has dramatically revolutionized the health care sector by helping radiologists toward an early and accurate diagnosis of the tumors. A brain tumor is an abnormal lump of tissues in which body cells grow and proliferate uncontrollably manner. In the world, the brain tumor is the second leading cause of cancer-related deaths in men in the age group of 20–45 and fifth leading cause among women. So, the detection of the tumor is a very important part of its treatment. This paper presents a comparative study of different classification techniques for extraction of tumorous images from non-timorous MRI images using thresholding segmentation technique and feature extraction using Gabor. The performance validation of the proposed system is evaluated in term of specificity, sensitivity, and classification accuracy using MRI images.
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Nitish, Singh, A.K., Singla, R. (2020). Different Approaches of Classification of Brain Tumor in MRI Using Gabor Filters for Feature Extraction. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_108
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DOI: https://doi.org/10.1007/978-981-15-0751-9_108
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