Main Article Content

Abstract

Cancer which can afflict brain also known as brain tumor is a superior of devastating diseases. It’s needs to
be recognized in early stages to counteract effectively. Segmentation of tumors in neural organ by human
capability is more prone to error and distinguishing between cancer cells from MRI tomography images is a
tedious and time taking process. Accuracy of these evaluations and segmentation plays an important role in
detection, treatment planning and how effective these measures are.Over the last 20 years in medical field
,automatic processing of images,computer vision and leasrning algorithms for machine usage garnered wide
attention of medical researchers and developers.These days in diagnosis of a disease capturing and
processing of medical images plays a huge role.Distinguishing between tumors of brain is a difficult job for
physicians.In this era of technological advancements,in detection of brain tumor and it’s type,machine
learning plays a critical role using different methods of ML(machine learning) with help of magnetic
resonance imaging(MRI).This paper tried to review the existing methods,algorithms and how effective they
are in detection and distinguishing the grade of tumor.This identification is carried out using various ML
methods of pre-processing,segmentation,featureextraction,classification and clustering of
supervised,unsupervised and deep learning methods.

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How to Cite
D, S., Kumar, S., Awasth, R. S., & Sravan, K. (2020). Brain Tumor Detection Using Machine Learning: Review. Think India Journal, 22(41), 65-70. Retrieved from https://thinkindiaquarterly.org/index.php/think-india/article/view/19260