Advancements In Fake Medical Image Detection: A Comparative Analysis Of YOLO, GAN, CNN, And Zero-Shot Learning Approaches
DOI:
https://doi.org/10.48047/46wxkt67Keywords:
Fake Image Detection, Medical Imaging, Zero-Shot Learning, Deep Learning, YOLO, Generative Adversarial Networks.Abstract
Background: The advancement of AI image manipulation in medical image with focus on orthopedic, especially on diagnosing of joints may lead to misdiagnosis and inappropriate treatment. Deep learning models like CNNs, GANs, YOLO and other require large amounts of prelabeled data which makes handling fake image detection difficult.
Downloads
References
Aldughayfiq, Bader, Farzeen Ashfaq, N. Z. Jhanjhi, and Mamoona Humayun. "Yolo- based deep learning model for pressure ulcer detection and classification." In Healthcare, vol. 11, no. 9, p. 1222. MDPI, 2023. [2] Baashirah, Rania. "Zero-Shot Automated Detection of Fake News: An Innovative Approach (ZS-FND)." IEEE Access (2024).
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
