Machine learning mechanism for segmentation, progressive assessment and prediction of brain tumor growth
Keywords:
bank, loan, attributes, categorical data, metrics, mobile applicationAbstract
A brain tumor is one type of illness caused by clots in the brain. A Magnetic Resonance Imaging (MRI) scan can be used to see a brain tumor in detail. Because of the similarity in color, it is difficult to distinguish brain tumor tissue from normal tissue. Brain tumors are common in all age groups, early and accurate identification of tumor type is critical for determining the most successful treatment regimens for each patient's situation. In today's world, computer interpretation of medical images plays a major role in medical diagnosis. Brain tumors can be accurately diagnosed with the use of advanced technologies. Early-stage diagnoses, as well as properly quantifying the amount and intensity of sickness, which were previously a barrier for medical science, are now eliminated by recent technologies. The automated technique for brain tumor identification was used in the study. The technique includes gray-scale conversion for reducing computation requirements. Filter operation was used to eliminate unwanted noises as much as possible to assist in improved segmentation. Next brain tumor segmentation, isolates tumor tissue from surrounding edema, fat, and cerebrospinal fluid. K-means clustering is used for this segmentation.
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