ISSN: 2277-405X
A Survey on Brain Tumor Prediction with Various Machine Learning Approaches
Paper ID: IJATRD-2026-00036
DOI :
DOI: https://doi.org/10.67750/ijatrd.v3.i2.36Keywords:
Keywords:
Abstract:
Abstract
Brain tumor is one of the most serious diseases that affects the human brain and can lead to severe health problems if not detected at an early stage. Traditional methods of brain tumor diagnosis mainly depend on MRI image analysis by medical experts, which may require more time and can sometimes lead to human errors. To improve the accuracy and speed of diagnosis, Machine Learning (ML) and Deep Learning (DL) techniques are widely used in the healthcare field. This survey paper presents an overview of various machine learning approaches used for brain tumor prediction and classification. Different algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree, Random Forest, Naïve Bayes, and Convolutional Neural Networks (CNN) are discussed in detail. The paper also explains important stages involved in brain tumor prediction, including image preprocessing, segmentation, feature extraction, and classification.
The main objective of this paper is to provide a clear understanding of existing brain tumor prediction methods and highlight future research directions for developing more accurate and reliable healthcare systems.
How to Cite
Tayade, S. & Sasankar, D. A. (2026, September 29).
A Survey on Brain Tumor Prediction with Various Machine Learning Approaches .
https://ijatrd.org/en/article/2026-00036
References:
References
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