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Detection of Brain Tumor Using K-means Clustering Algorithm

Dhinakaran M., Isha Jaiswal, Ishita Rawat, Jatin Belani

Abstract


A brain tumor is a dangerous medical illness that, if not treated promptly, can be fatal. Therefore, it becomes essential to find the tumor early on in order to organize therapy as soon as possible. A brain tumor is the result of the growth of abnormal cells in the brain. Brain tumors come in many different forms. The majority of brain tumors are malignant (cancerous); however, some are benign (noncancerous). Due to its non-invasive nature and superior ability to depict internal tumor information, MRI is preferred among all other imaging modalities. Moreover, MRI was utilized to develop clear images of the body’s components. By using the proposed methodology, we are able to detect brain tumor and their location. Three stages make up the proposed methodology—Image acquisition: A data set’s MRI picture is used. Pre-processing: The image is changed to grayscale at this stage, and then the median filter is used to get rid of the impulse noise present in the image. Post-processing: At this phase, the K means clustering technique is used to segment the image, and features from the image are extracted utilizing morphological processes. K means clustering, a method for unsupervised learning, divides the unlabeled data set into various clusters, where “k” is the number of clusters. With the help of the MATLAB program, a graphical user interface for brain tumor detection is created by us. These tools alter the grey level and include additional unique filters in an effort to enhance the MRI image’s quality.


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References


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