Published January 30, 2025
| Version CC-BY-NC-ND 4.0
Journal article
Open
Diagnosis of Abdominal Diseases Affecting Major Organs Using CT Image and YOLOV8
- 1. Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.
- 1. Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.
Description
Abstract: This study investigates the YOLOv8 method, a popular object detection model, to detect abnormalities in abdominal CT scans. Our study leverages the sophisticated architecture and point-of- care detection capabilities of YOLOv8 to show that the model improves diagnostic accuracy and helps radiologists quickly identify potential panic cases.
Files
B105005020125.pdf
Files
(281.1 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:2c1cab512d467b203de86c4ddd39040f
|
281.1 kB | Preview Download |
Additional details
Identifiers
- DOI
- 10.54105/ijpmh.B1050.05020125
- EISSN
- 2582-7588
Dates
- Accepted
-
2025-01-15Manuscript received on 21 November 2024 | First Revised Manuscript received on 27 November 2024 | Second Revised Manuscript received on 14 December 2024 | Manuscript Accepted on 15 January 2025 | Manuscript published on 30 January 2025.
References
- Sabri Koçer; Omar Mohamed; Özgür Dündar (2024) "Disease Detection in Abdominal CT Images Using the YOLOv5 Algorithm: A Deep Learning Approach" DOI: https://dx.doi.org/10.1109/ICEST62335.2024.10639613
- Litjens, G., et al. (2017). "A survey on deep learning in medical image analysis." Medical Image Analysis, 42, 60-88. DOI: https://doi.org/10.1016/j.media.2017.07.005
- Redmon, J., et al. (2016). "You Only Look Once: Unified, Real- Time Object Detection." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. DOI: https://doi.org/10.1109/CVPR.2016.91
- Bochkovskiy, A., et al. (2020). "YOLOv4: Optimal Speed and Accuracy of Object Detection." arXiv preprint arXiv:2004.10934. DOI: https://dx.doi.org/10.48550/arXiv.2004.10934
- Priyanka Israni, Maulika S. Patel, Medical Image Analysis (MedIA) using Deep Learning. (2020). In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 7S, pp. 21–25). DOI: https://doi.org/10.35940/ijitee.g1007.0597s20
- . C. Devi Parameswari, K. Shankar, Medical Image Security - the Stateof-the-Art. (2019). In International Journal of Recent Technology and Engineering (Vol. 8, Issue 4S2, pp. 542–545). DOI: https://doi.org/10.35940/ijrte.d1101.1284s219
- Akila, Mrs. P. G., Batri, K., Sasi, G., & Ambika, R. (2019). Denoising of MRI Brain Images using Adaptive Clahe Filtering Method. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1s, pp. 91–95). DOI: https://doi.org/10.35940/ijeat.a1018.1091s19
- Saha, T., & Vishal, Dr. K. (2024). A Study of Application of Digital Image Processing in Medical Field and Medical Image Segmentation by Edge Detection. In International Journal of Emerging Science and Engineering (Vol. 12, Issue 4, pp. 3–8). DOI: https://doi.org/10.35940/ijese.g9890.12040324