ItemArticle

Identification of Brain Tumors Using CNN and ML with Diverse Feature Selection Techniques

Title
Identification of Brain Tumors Using CNN and ML with Diverse Feature Selection Techniques
Creator
Singh, Suraj Pal; Upreti, Kamal; Jain, Rituraj; Arora, Rakesh Kumar; Tiwari, Akhilesh; Radhakrishnan, G.V.
Description
Early diagnosis and treatment is very essential in monitoring Brain tumor using MRI images. Convolutional Neural Networks (CNN) and Machine Learning (ML) classifiers have been widely used but there is not much work on how feature selection techniques would affect the performance of the CNN. Secondly, there is a need for investigation concerning small dataset adaptability and ML-CNN comparisons. To improve the classification accuracy, we integrate Univariate, Recursive Feature Elimination (RFE), Recursive Feature Elimination with Cross Validation (RFECV) with CNN in this study. Preprocessing, feature extraction & selection was carried out on the dataset consisting of 253 MRI images and they are classified using CNN and ML models (Logistic Regression, Decision Tree, Random Forest, Nae Bayes). With the results 96%, CNN with Univariate Feature Selection performed better than ML classifiers, and other selection techniques. The results demonstrate that feature selection is necessary to get the best performance out of CNN models operating on small datasets. Future studies should be based on different deep learning architectures to improve classification and application i n other datasets. 2025, Interdisciplinary Publishing Academia. All rights reserved.
Source
Journal of Applied Science and Technology Trends;Volume;6;Issue;1;pp.74-86
Date
01-01-2025
Publisher
Interdisciplinary Publishing Academia
Subject
brain tumor detection; CNN; feature selection; machine learning; medical imaging; MRI classification
Coverage
Singh S.P., Computer Science and Engineering, Chandigarh University, Mohali, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India; Arora R.K., Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India; Tiwari A., Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India; Radhakrishnan G.V., Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 27080757;
Format
online
Language
English
Type
Article

Citation

Singh, Suraj Pal; Upreti, Kamal; Jain, Rituraj; Arora, Rakesh Kumar; Tiwari, Akhilesh; Radhakrishnan, G.V., “Identification of Brain Tumors Using CNN and ML with Diverse Feature Selection Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/23575.

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