Brain Tumor Segmentation Using K-means Clustering Algorithm (original) (raw)

With expanding technologies around the world, the medical field is also adapting new mechanization to perform treatment effectively. Identification of brain tumors with old technologies like MRI (magnetic resonance image), CT (Computer Tomography) scan takes time to confirm the possibility of the abnormal cell being cancerous or non-cancerous. Any abnormal cell or mass collection in the brain is a brain tumor. The possibility of a brain tumor depends on the abnormal cell's benign (non-cancerous) or malignant (cancerous) nature. In this paper to differentiate between the benign and malignant abnormal cells, one of the extensively used machine learning algorithm K-mean clustering is used for the implementation of the model. Kmean clustering is unsupervised learning, where centroids are defined to make data as clusters having relative relation between them. The agenda of this paper is to examine the abnormal cell whether it is benign (non-cancerous) or malignant (cancerous) using K-mean clustering effectively. In this paper, BRATS 2018 dataset is used for proposed methodology. After implementing proposed methodology, on basis of MR images it is differentiated between tumor being cancerous and non-cancerous.

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