Open AccessAnalysis of machine learning algorithms in brain tumour prediction
Author(s) -
Manali Gupta,
Sanjay Kumar Sharma,
Roshi Saxena,
Sahil Arora
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2070/1/012090
Subject(s) - consistency (knowledge bases) , computer science , artificial intelligence , machine learning , magnetic resonance imaging , algorithm , medicine , radiology
The tumour is fundamentally an excessive development of dangerous cells in any part of the body, while a tumour in a brain is an unreasonable development of cancerous cells in the brain. Brain tumour can be either benign or malignant. The benign brain tumour has structural consistency and does not include active (cancer) cells, but the malignant brain tumour has no structure consistency and includes active cells. The primary concern is to segment, detect, and extract the infected tumour area from magnetic resonance images (MRI) which are being performed by radiologists or medical experts, and their accuracy is totally dependent on their experience only. Thus, it becomes very essential to overcome these limitations by the use of artificial intelligence. The current paper uses various machine learning algorithms as well as their features to design a structure to predict brain tumour at an early phase by using different classifiers and comparing their respective accuracy parameters.