Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)

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Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images

Authors
Paleti Nikhil Chowdary1, *, Gorantla V. N. S. L. Vishnu Vardhan1, Menta Sai Akshay1, Menta Sai Aashish1, Vadlapudi Sai Aravind1, Garapati Venkata Krishna Rayalu1, P. Aswathy1
1Amrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Coimbatore, India
*Corresponding author. Email: nikhil.paleti@outlook.com
Corresponding Author
Paleti Nikhil Chowdary
Available Online 21 December 2023.
DOI
10.2991/978-94-6463-314-6_27How to use a DOI?
Keywords
Deep Learning; Computer Vision; Knee Osteoarthritis (OA); CLAHE; Data Augmentation; Diffusion Models
Abstract

This research paper explores the classification of knee osteoarthritis (OA) severity levels using advanced computer vision models and augmentation techniques. The study investigates the effectiveness of data preprocessing, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), and data augmentation using diffusion models. Three experiments were conducted: training models on the original dataset, training models on the preprocessed dataset, and training models on the augmented dataset. The results show that data preprocessing and augmentation significantly improve the accuracy of the models. The EfficientNetB3 model achieved the highest accuracy of 84% on the augmented dataset. Additionally, attention visualization techniques, such as Grad-CAM, are utilized to provide detailed attention maps, enhancing the understanding and trustworthiness of the models. These findings highlight the potential of combining advanced models with augmented data and attention visualization for accurate knee OA severity classification.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
21 December 2023
ISBN
10.2991/978-94-6463-314-6_27
ISSN
2589-4900
DOI
10.2991/978-94-6463-314-6_27How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Paleti Nikhil Chowdary
AU  - Gorantla V. N. S. L. Vishnu Vardhan
AU  - Menta Sai Akshay
AU  - Menta Sai Aashish
AU  - Vadlapudi Sai Aravind
AU  - Garapati Venkata Krishna Rayalu
AU  - P. Aswathy
PY  - 2023
DA  - 2023/12/21
TI  - Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 266
EP  - 274
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_27
DO  - 10.2991/978-94-6463-314-6_27
ID  - Chowdary2023
ER  -