Vol. 6 No. 1 (2025)

Standard Journal Issues

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

Published 2024-06-30

  • Suraj Pal Singh
    • ,
  • Kamal Upreti
    • ,
  • Rituraj Jain
    • ,
  • Rakesh Kumar Arora
    • ,
  • Akhilesh Tiwari
    • ,
  • G V Radhakrishnan

Suraj Pal Singh
Computer Science and Engineering, Chandigarh University, Mohali, India

Kamal Upreti
Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India

Rituraj Jain
Department of Information Technology, Marwadi University, Rajkot, Gujarat, India

Rakesh Kumar Arora
Computer Science and Engineering, Dr. Akhilesh Das Gupta Institute of Professional Studies, Delhi, India

Akhilesh Tiwari
Department of Business and Management, Christ University, Delhi NCR Campus, Ghaziabad, India

G V Radhakrishnan
Kalinga School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India

Abstract

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, Naïve 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 in other datasets.

Keywords

brain tumor detection, CNN, feature selection, machine learning, medical imaging, MRI classification

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