Binary Classification of Brain Tumor using Early and Late fusion

Authors

  • Shlok Nandurbarkar Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis Internation (Deemed University), Pune, India.
  • Abhyuday Singh Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis Internation (Deemed University), Pune, India.
  • Himanshu Bute Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis Internation (Deemed University), Pune, India.
  • Nandhini K. Department of Artificial Intelligence and Machine Learning, Symbiosis Institute of Technology, Symbiosis Internation (Deemed University), Pune, India.
  • Shilpa Gite Symbiosis Centre for Applied AI, Symbiosis International (Deemed University), Pune, India.
  • Kumar Rajamani Senior Manager Algorithms, KLA-Tencor, India.

Keywords:

Brain Tumor, CNN, Glioblastoma Multiforme, Binary Classification

Abstract

This research delves into the realm of medical imaging and artificial intelligence to enhance the classification of brain tumors, specifically distinguishing between Grade III and Grade IV gliomas. Leveraging the TCGA-GBM dataset, encompassing various image modalities such as Flair, T1, T1ce, T2, and Mask, acquired through magnetic resonance imaging (MRI), the study explores the efficacy of deep learning techniques. Both early and late fusion strategies are employed to amalgamate information from diverse modalities. The convolutional neural network (CNN)-based models exhibit commendable performance in accurately categorizing glioma types, showcasing promise for potential applications in clinical diagnostics.

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Published

07.01.2024

How to Cite

Nandurbarkar, S. ., Singh, A. ., Bute, H. ., K., N. ., Gite, S. ., & Rajamani, K. . (2024). Binary Classification of Brain Tumor using Early and Late fusion. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 402–414. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4389

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

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