LIVER ULTRASOUND IMAGING LESION DETECTION BASED ON YOLO

Chien-Hsing Huang

doi:10.29121/ijetmr.v11.i7.2024.1475

LIVER ULTRASOUND IMAGING LESION DETECTION BASED ON YOLO

Authors

Chien-Hsing Huang Department of Information Engineering, I-Shou University, Kaohsiung City, Taiwan, P.O.C. https://orcid.org/0000-0002-6785-070X

DOI:

https://doi.org/10.29121/ijetmr.v11.i7.2024.1475

Keywords:

Artificial Intelligence, Machine Learning, Ultrasound Imaging, Medical Image, Object Detection, YOLOv4

Abstract

The liver is a silent organ with no pain-sensing nerves. When the body's functions begin to appear abnormal, it may have entered the trilogy of liver diseases: "hepatitis, cirrhosis, and liver cancer." An abdominal ultrasound is a powerful tool for checking liver health. With the rapid development of ultrasound technology, ultrasound machines are gradually developing in the direction of miniaturization and cheapness, transforming from hospital-specific equipment to home medical equipment. However, ultrasound diagnosis requires professional knowledge, which has also become the threshold for the popularization of ultrasound diagnosis. This article uses artificial intelligence and machine learning technology to take the liver ultrasound images marked by professional doctors as a data set. After training of image object detection model YOLO, it can be used to detect tumors, hepatic hemangioma, radiofrequency cautery, abscess and metastatic, the accuracy can reach 98%.

Downloads

Download data is not yet available.

References

Chan, V., & Perlas, A. (2011). Basics of Ultrasound Imaging. Atlas of Ultrasound-Guided Procedures in Interventional Pain Management, 13-19. https://doi.org/10.1007/978-1-4419-1681-5_2

Gupta, A. K., Seal, A., Prasad, M., & Khanna, P. (2020). Salient Object Detection Techniques in Computer Vision - A Survey. Entropy, 22(10). https://doi.org/10.3390/e22101174

Jiang, P., Ergu, D., Liu, F., Cai, Y., & Ma, B. (2022). A Review of Yolo Algorithm Developments. Procedia Computer Science, 199, 1066-1073. https://doi.org/10.1016/j.procs.2022.01.135

Jiang, Z., Zhao, L., Li, S., & Jia, Y. (2020). Real-Time Object Detection Method Based on Improved YOLOv4-tiny. https://doi.org/10.48550/arXiv.2011.04244

Powers, J., & Kremkau, F. (2011). Medical Ultrasound Systems. Interface Focus, 1(4), 477-489. https://doi.org/10.1098/rsfs.2011.0027

Rumack, C. M., & Levine, D. (2023). Diagnostic Ultrasound. Elsevier Health Sciences.

Shafiee, M. J., Chywl, B., Li, F., & Wong, A. (2017). Fast YOLO: A Fast You Only Look Once System for Real-Time Embedded Object Detection in Video.

Torralba, A., Russell, B. C., & Yuen, J. (2010). Labelme: Online Image Annotation and Applications. Proceedings of the IEEE, 98(8), 1467-1484.

Wells, P. N. (2006). Ultrasound Imaging. Physics in Medicine & Biology, 51(13), R83. https://doi.org/10.1088/0031-9155/51/13/R06

Williams, R. (2006). Global Challenges in Liver Disease. Hepatology, 44(3), 521-526. https://doi.org/10.1002/hep.21347

Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023). Object Detection in 20 Years: A Survey. Proceedings of the IEEE, 111(3), 257-276. https://doi.org/10.1109/JPROC.2023.3238524

Downloads

pdf
html

Published

2024-07-24

How to Cite

Huang, C.-H. (2024). LIVER ULTRASOUND IMAGING LESION DETECTION BASED ON YOLO. International Journal of Engineering Technologies and Management Research, 11(7), 8–13. https://doi.org/10.29121/ijetmr.v11.i7.2024.1475

Download Citation

Issue

Vol. 11 No. 7 (2024): IJETMR: Volume 11 Issue 7 July, 2024

Section

Articles

License

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

License and Copyright Agreement

In submitting the manuscript to the journal, the authors certify that:

They are authorized by their co-authors to enter into these arrangements.
The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
That it is not under consideration for publication elsewhere.
That its release has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
They agree to the following license and copyright agreement.

Copyright

Authors who publish with International Journal of Engineering Technologies and Management Research agree to the following terms:

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or edit it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

For More info, please visit CopyRight Section