A CNN-Driven Approach for Injury Type and Severity Detection with Hospital Recommendations for Emergency Response

Authors

  • Dr. M. Swapna, Anumasa Asrith Sai, Martha Madhav, Guguloth Vijay, S. Madhu Kumar

Keywords:

Keywords: Health care systems, Emergency responses, Image classification, Recommendation system, Deep learning, Convolutional neural networks.

Abstract

Traumatic injuries are a leading cause of emergency department visits and can rapidly progress tolife-threatening conditions without prompt, accurate assessment. In many pre-hospital andresource-limited settings, first responders and clinicians lack immediate access to specialists oradvanced diagnostic tools, resulting in inconsistent triage decisions and treatment delays. There is anurgent need for an automated, image-based solution that standardizes injury evaluation, enabling rapid,data-driven support for emergency response teams

References

Litjens, G., Kooi, T., Bejnordi, B. E., et al. (2017). "A survey on deep learning in medical image analysis." Medical Image Analysis, 42: 60–88. https://doi.org/10.1016/j.media.2017.07.005

Goyal, N., Siddhartha, K., & Pant, M. (2021). "Deep Learning for Wound Analysis: A Comprehensive Review." IEEE Access, 9: 83992–84021. https://doi.org/10.1109/ACCESS.2021.3084562

Downloads

Published

2025-04-22

How to Cite

Dr. M. Swapna, Anumasa Asrith Sai, Martha Madhav, Guguloth Vijay, S. Madhu Kumar. (2025). A CNN-Driven Approach for Injury Type and Severity Detection with Hospital Recommendations for Emergency Response . Journal of Computational Analysis and Applications (JoCAAA), 34(4), 210–221. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2298

Issue

Section

Articles

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.