BRAIN TUMOR DETECTION THROUGH IMAGE SEGMENTATION
Author(s):
Vinaya H Naikar, Rhea Nataraj, Shreya H M, Dr. Savita Choudhary
Keywords:
Brain tumor detection, Machine learning, CNN.
Abstract
In today's world of medicine, brain tumor detection has become a common occurrence. A brain tumor is a deformed mass of tissue in which the cells expand rapidly and without control, i.e. there is no control over the cells' growth. The image segmentation method is used to extract aberrant tumor patches from the brain. The segmentation of brain tissue in an MRI (magnetic resonance image) is critical for detecting the existence of outlines related to a brain tumor. In the health-care industry, there is a wealth of unreleased data. Early disease prediction can be accomplished with the proper application of reliable data mining categorization algorithms. In the realm of medicine, the procedures of Machine learning (ML) and data mining both have a substantial presence. The vast majority of which is efficiently implemented. The study looks at a list of risk factors that have been identified through brain tumor surveillance systems. In addition, the suggested method guarantees that brain tumor detection, classification, and segmentation would be exceedingly efficient and exact. Precision automatic or semiautomatic methods are required to achieve this. The study provides a method for automatic segmentation that uses CNN (Convolution Neural Networks) to determine small 3 x 3 kernels. Segmentation and classification are achieved by combining this single technique. CNN (a machine learning technique) is derived from NN (Neural Networks), and it has a layer for the classification of outcomes. Significant relationships and patterns can be retrieved from data using DM (data mining) techniques. Machine learning and data mining techniques are being successfully used to detect and prevent brain tumors at an early stage.
Article Details
Unique Paper ID: 153835

Publication Volume & Issue: Volume 8, Issue 9

Page(s): 56 - 59
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