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Brain Tumor Detection using Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 9 |
| Year of Publication: 2023 |
| Authors: Kushagra, Kushagra Agrawal, Lakshay Goel |
10.5120/ijca2023922747
|
Kushagra, Kushagra Agrawal, Lakshay Goel . Brain Tumor Detection using Machine Learning. International Journal of Computer Applications. 185, 9 ( May 2023), 24-27. DOI=10.5120/ijca2023922747
Abstract
A brain tumor is the growth of brain cells that are abnormal, some of which may progress into cancer. Brain tumors are frequently discovered via MRI (magnetic resonance imaging) scans[1]. The MRI images reveal aberrant tissue development in the brain. A lot of research articles employ deep and machine-learning algorithms to detect brain cancer. In several research articles, deep and machine-learning algorithms are used to identify brain tumors. When these algorithms are applied to MRI images, it only takes a very short amount of time to predict a brain tumor, and the increased accuracy makes patient treatment simpler.[1][2] These forecasts enable the radiologist to make quick decisions. In the proposed study, brain cancers are detected using self-defined artificial neural networks (ANN) and convolution neural networks (CNN), and their performance is evaluated. This paper's goal is to give a thorough examination of recent advances in techniques like deep learning, preprocessing, and machine learning and use that information to present a thorough comparative comparison. The difficulties that researchers have had in the past while attempting to identify tumors have been explored, along with potential future study areas. The clinical difficulties that are faced have also been covered, something the previous review papers neglect.
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