A New Approach to Brain Tumor Detection with CNNS: Addressing the Issues of Standardization and Generalizability

Authors

  • Pradnya Mehta Dept. of Computer Engineering-AI, Vishwakarma Institute of Information Technology, Pune, India
  • Sanved Narwadkar Dept. of Information Technology, Vishwakarma Institute of Information Technology, Pune, India
  • Geetha Chillarge Dept. of Computer Engineering, Marathwada Mitra Mandal’s College of Engineering, Pune, India India
  • Snehal Rathi Dept. of Computer Engineering-AIML, Vishwakarma Institute of Information Technology, Pune, India
  • Geetanjali Shinde Dept. of Computer Engineering-AIML, Vishwakarma Institute of Information Technology, Pune, India
  • Chaitali Shewale Dept. of Information Technology, Vishwakarma Institute of Information Technology, Pune, India

Keywords:

brain tumor detection, CNNs, standardization, generalizability, preprocessing techniques

Abstract

Brain tumour detection is a key task in medical imaging that necessitates precise and dependable approaches for early detection and treatment. Among imaging modalities, MRI is the gold standard for spotting malignant growths in the brain. Brain tumour size, shape, and location can all be discerned from MRI scans of the brain. Brian tumour detection can be done with visual analysis, medical image processing or computer aided detection. The motivation for this study is the current lack of universally applicable methods for detecting brain tumours. The lack of standardisation in brain images is a major challenge for current CNN models, typically resulting in subpar performance and poor generalizability. As a result, the goal of this research is to establish a procedure that will help standardise and broaden the applicability of CNN-based brain tumour detection of specific type. This research aims to improve generalizability by utilising a CNN models on large-scale datasets, and increase standardisation in brain images by incorporating robust preprocessing techniques, such as standardisation, feature extraction,segmentation etc. To test the performance of our proposed method with several deep learning techniques, including support vector machine (SVM) and random forest algorithm, we accomplished extensive experiments on an enormous data set comprising of brain scans from a broad range of sources. The outcomes show substantial gains in precision and generalizability over the current gold standard. The overall classification accuracy of CNN algorithm for barin tumor detection is 98.28%.

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Published

13.12.2023

How to Cite

Mehta, P. ., Narwadkar, S. ., Chillarge, G. ., Rathi, S. ., Shinde, G. ., & Shewale, C. . (2023). A New Approach to Brain Tumor Detection with CNNS: Addressing the Issues of Standardization and Generalizability. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 01–13. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4082

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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