A Model for Brain Tumor Detection Using a Modified Convolution Layer ResNet-50

Authors

  • Rakesh Kumar Yadav Associate Professor, Department of CSE, MSOET, Maharishi University of Information Technology, Uttar Pradesh, India
  • Abhishek Kumar Mishra Associate Professor, Department of CSE, IFTM University, Moradabad, Uttar Pradesh, India
  • Dilip Kumar Jang Bahadur Saini Assistant Professor, Department of CSE, Pimpri Chinhwad University, Pune, Maharashtra, India
  • Hemlata Pant Assistant Professor, Department of CSE, Pranveer Singh Institute of Technology, Kanpur, Uttar Pradesh, India
  • R. G. Biradar Professor, Pimpri Chinhwad University, Pune, Maharashtra, India
  • Pranati Waghodekar Assistant Professor, Department of CSE, MIT WPU, Pune, Maharashtra, India

DOI:

https://doi.org/10.51983/ijiss-2024.14.1.3753

Keywords:

CNN, Brain Tumour, MRI, Deep Learning

Abstract

Tumors are the second most prevalent type of cancer, posing a serious concern to many individuals due to their unregulated tissue development. Efficient approaches for identifying tumors, particularly brain cancer, quickly, automatically, precisely, and correctly, are crucial in the medical industry. When cancer is appropriately recognized, early identification plays a critical role in effective treatment, ensuring patient safety. Tumors form as a result of uncontrolled cell development, causing the slow degeneration of brain tissue as they consume resources meant for healthy cells and tissues. While Magnetic Resonance Imaging (MRI) is used to examine images to establish tumor location and size, the procedure is inefficient and time-consuming. The suggested model’s key tool is the Convolutional Neural Network (CNN) model ResNet-50, which achieves an impressive accuracy rate of 81.6 percent. As expected, the model’s performance exceeds expectations.

References

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Published

16-02-2024

How to Cite

Yadav, R. K., Mishra, A. K., Jang Bahadur Saini , D. K., Pant, H., Biradar, R. G., & Waghodekar, P. (2024). A Model for Brain Tumor Detection Using a Modified Convolution Layer ResNet-50. Indian Journal of Information Sources and Services, 14(1), 29–38. https://doi.org/10.51983/ijiss-2024.14.1.3753

Issue

Vol. 14 No. 1 (2024): January-March 2024

Section

Articles

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