Deep Learning Based Brain Tumor Analysis with Manual Layer Selection

Authors

  • B. V. Subbayamma Assistant Professor, Department of ECE, Prasad V Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, India
  • N. Nandhini Assistant Professor, Departemnt of IT, P.S.V College of Engineering and Technology, Krishnagiri, Tamilnadu, India.
  • Mahesh Maurya Associate Professor and Head, Department of Computer Engineering, K.C. College of Engineering, Mith Bunder road, Kopri, Thane East, Maharashtra, India.
  • Theetchenya S. Assistant Professor, Department of Computer Science and Engineering, Sona College of Technology, Junction Main Road, Salem, India.
  • S. Ramasamy Associate Professor, Department of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, India.
  • Shikha Maheshwari Associate Professor, Directorate of Online Education, Manipal University Jaipur, Rajasthan, India.

Keywords:

Deep Learning, Cancer, AI techniques, VCG, Extraction, Machine Learning

Abstract

Computer scientists in the field of artificial intelligence (AI) aim to build computers and Programmes with cognitive abilities that mimic those of humans in areas such as voice recognition, learning, planning, and problem solving. The term "deep learning" refers to a set of algorithms in machine learning; these algorithms are a subset of a larger family of approaches for machine learning based on "learning representations" of data. In order to facilitate the quick and simple diagnosis and identification of brain tumours, deep learning is employed as a way to generate detection and classification models utilizing MRI imaging. Using deep learning methods, a model for detecting brain tumours will be developed and discussed in this thesis. Finding an efficient method of detecting brain tumours using MRI to aid in the brain doctor's ability to make quick, correct judgments is the objective. According to a study released by the World Health Organization in 2021, Asia has the greatest mortality rate from CNS diseases such brain cancer. The key to saving many of these lives is early cancer detection. The model has been developed and deployed, and it makes use of a dataset including 10,000 photos to identify brain tumours using Deep Learning methods. Proposed Work has achieved the accuracy level of 98.4%.

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References

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Published

05.12.2023

How to Cite

Subbayamma, B. V. ., Nandhini, N. ., Maurya, M. ., S., T. ., Ramasamy, S. ., & Maheshwari, S. . (2023). Deep Learning Based Brain Tumor Analysis with Manual Layer Selection. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 20–25. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4019

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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