Detection Brain Tumor Using CNN and YOLO

Authors

  • Bommisetty Naveen  Department of Electronics and Communication Engineering, Audisankara College of Engineering and Technology, Gudur, India
  • Mandi Prathyusha  Department of Electronics and Communication Engineering, Audisankara College of Engineering and Technology, Gudur, India
  • Damai Lokesh  Department of Electronics and Communication Engineering, Audisankara College of Engineering and Technology, Gudur, India
  • Kunche Sathwika  Department of Electronics and Communication Engineering, Audisankara College of Engineering and Technology, Gudur, India
  • Himabindu Sathyaveti  Department of Electronics and Communication Engineering, Audisankara College of Engineering and Technology, Gudur, India

Keywords:

Convolutional Neural Network, YOLO, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT).

Abstract

Different multimodalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), are mashed together in the medical field to create a fused image. Image fusion (IF) is a method for preserving crucial information by combining all pertinent details from numerous photographs into a single fused image. Brain CT and MRI scans are merged in this study and labelled as normal or abnormal. If the connection network is discovered to be abnormal, the portion of the cancer region is determined. These demonstrations make use of YOLOV2 architecture, convolutional neural networks, and image processing methods. The experimental results are evaluated based on model accuracy.

References

  1. Kaur H, Rani E J, “Analytical comparison of various image fusion techniques”, International and Software Engineering 5 (2015).
  2. Garg R, Gupta P, Kaur H, “Survey on multi-focus image fusion algorithms”, Recent Advances in Engineering and Computational Sciences (RAECS) (2014) pp. 1-5.
  3. Bavirisetti DP, Kollu V, Gang X, Dhuli R, “Fusion of MRI and CT images using guided image filter and image statistics”, International Journal of Imaging Systems and Technology. 2017 Sep;27(3):227-37.
  4. Jędrzej Świeżewski, Ph.D, YOLO Algorithm & Object Detection, Appsilon, Copyrights © appsilon.com, 10 October 2020, https://appsilon.com/object-detection-yolo-algorithm/
  5. Jason Brownlee, “Convolutional Neural Network Model Innovations for Image Classification”, Machine Learning Mastery, © 2021 Machine Learning Mastery, July 5, 2019. https://machinelearningmastery.com/review-of-architectural-innovations-for-convolutional-neural-networks-for-image-classification/
  6. Rohit Gandhi, “Support Vector Machine — Introduction to Machine Learning Algorithms”, https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca4

International Journal of Scientific Research in Science and Technology

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Published

2023-06-30

Issue

Volume 10, Issue 3, May-June-2023

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Bommisetty Naveen, Mandi Prathyusha, Damai Lokesh, Kunche Sathwika, Himabindu Sathyaveti "Detection Brain Tumor Using CNN and YOLO" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 3, pp.151-158, May-June-2023.