Abstract
Brain tumour is one of the threatening malignancies for human beings. Tumour exists as a mass in the brain. Hence detection of the tumour is more important before providing the respective treatment. This paper deals with improved system for brain tumour detection and classification. Medical imaging is an essential phase in the detection of malignancy within the human body. In case of cancer, imaging becomes inevitable as the mode of treatment itself relies on nature of the tumour. The fundamental modalities employed in clinic includes the Computed Tomography (CT), Ultrasound, and Magnetic Resonance Imaging (MRI). In CT scan images due to poor soft tissue contrast, extraction of the tumour segment becomes difficult and it is also challenging to detect lesions. As with ultrasound it cannot be used for detection of cancer and lacks accuracy. Hence MRI images are taken into consideration which overcomes these limitations and allows functional imaging. By using MRI, cancer staging can also be determined. An MRI image of the brain tumour is taken as the input which is segmented and classified sequentially. Each step employs well defined algorithms paving its way to provide accurate results even for haphazard MRI images. It bates the problem of analysing the poor quality MRI images. Segmentation method using adaptive k-means clustering divides the MRI image into multiple segments from which a meaningful extract of the brain tumour is obtained. Finally, the segmented image is classified using Support Vector Machine classifier. This classifier determines the type of the tumour. When three kernel functions of the SVM classifier is compared, the linear kernel yields the result with higher accuracy.
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Sharath Chander, P., Soundarya, J., Priyadharsini, R. (2020). Brain Tumour Detection and Classification Using K-Means Clustering and SVM Classifier. In: P. P. Abdul Majeed, A., Mat-Jizat, J., Hassan, M., Taha, Z., Choi, H., Kim, J. (eds) RITA 2018. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-8323-6_5
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