Advanced CNN Detection Method for Brain Tumor Analysis

Authors

  • Vishal Gangadhar Puranik Assistant Professor, School of Electronics and Telecommunication Engineering, MIT Academy of Engineering,Alandi, Pune, Maharashtra, India.
  • S. Edwin Raja Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamilnadu, India.
  • Naveen Kumar G. N. Associate Professor, Department of Electronics and Communication Engineering, CMR Institute of Technology, Bengaluru, Karnataka, India.
  • D. Sugumar Associate Professor, Department of ECE, Karunya Institute of Technology and Sciences (Deemed to be University), Coimbatore, Tamil Nadu, India.
  • Savanam Chandra Sekhar Associate Professor, KL Business School, Koneru Lakshmaiah Education Foundation, KL University, Vaddeswaram, Guntur, Andhra Prades, India.
  • Jayaraj Ramasamy Senior Lecturer, Faculty of Engineering and Technology, Botho University, Botswana.

Keywords:

CNN, Brain Tumor, Mixture of Images, RNN, Machine Learning

Abstract

Intense cancer of the brain is a leading cause of death. So, it's clear that early diagnosis is essential for effective therapy. As the science of deep learning has advanced recently, it has made significant contributions to medical diagnosis in the healthcare sector. Brain tumor detection using MRI images has seen extensive application of the deep learning technique known as convolutional neural networks (CNNs). Improved deep learning algorithms and more effective CNNs are needed because of the little dataset available. As a result, Data Augmentation is one of the most well-known methods for enhancing model performance. In this study, we provide an in-depth comparison of several CNN architectures and describe the salient features of popular models including VGG. We then offer a CNN- and data-augmented technique for effectively identifying brain cancers in MRI datasets. With its deep architectural design and high detection success, the suggested method has been shown to be an improvement over earlier research, as measured by evaluation criteria. Training, test, and validation data comprised of human brain pictures with and without malignancies. Our suggested work's findings reveal that, in comparison to preexisting models, the Proposed architecture achieves a value of 98% accuracy.

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Published

05.12.2023

How to Cite

Puranik, V. G. ., Raja, S. E. ., G. N., N. K. ., Sugumar, D. ., Sekhar, S. C. ., & Ramasamy, J. . (2023). Advanced CNN Detection Method for Brain Tumor Analysis . International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 250–255. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4068

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

License

Copyright (c) 2023 Vishal Gangadhar Puranik, S. Edwin Raja, Naveen Kumar G. N., D. Sugumar, Savanam Chandra Sekhar, Jayaraj Ramasamy

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