• Devansh Kumar
• Leninisha Shanmugan
• Ayushi Jain

The uncontrolled growth of cells inside the brain or skull causes a brain tumour, which is a life-threatening neurological disorder. People with this disease are dying at an alarmingly high rate. Early detection of malignant tumours is crucial for patient treatment, because early detection increases the patient's chances of survival. If a patient is not properly treated, their chances of survival are usually very low. If a brain tumour is not detected early enough, it will almost certainly result in death. As a result, the use of an automated technology is required for early diagnosis of brain cancers. A major challenge is the segmentation, identification, and isolation of contaminated tumour regions from magnetic resonance (MR) images. However, radiologists or clinical specialists must go through a difficult and time-consuming process, and their performance is totally dependent on their competence. The employment of computer-assisted approaches becomes crucial to overcome these constraints.

Brain Tumor, Image Segmentation, Deep Network, Machine Learning, MRI

"ANALYSIS OF RECENT BRAIN TUMOR DETECTION TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 1, page no.b14-b19, January-2022, Available :http://www.jetir.org/papers/JETIR2201103.pdf

"ANALYSIS OF RECENT BRAIN TUMOR DETECTION TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 1, page no. ppb14-b19, January-2022, Available at : http://www.jetir.org/papers/JETIR2201103.pdf