95% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
ORIGINAL RESEARCH article
Front. Med.
Sec. Pulmonary Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1567119
This article is part of the Research Topic Bridging Tradition and Future: Cutting-edge Exploration and Application of Artificial Intelligence in Comprehensive Diagnosis and Treatment of Lung Diseases View all 6 articles
Deep Learning Object Detection-based Early Detection of Lung Cancer
Provisionally accepted- 1 Changhua Christian Hospital, Changhua, Taiwan
- 2 National Chung Hsing University, Taichung, Taiwan
- 3 Yuanlin Christian Hospital, Yuanlin, Changhua County, Taiwan
- 4 Chaoyang University of Technology, Taichung, Taiwan
Early diagnosis and accurate classification of lung cancer exerts a critical impact on clinical treatment and patient survival. The rise of artificial intelligence technology has brought breakthroughs in medical image analysis. This study utilizes the Lung-PET-CT-Dx public dataset for conducting model training and evaluation. The performance of the You Only Look Once (YOLO) series of models in the lung CT image object detection task is compared in terms of algorithms, and different versions of YOLOv5, YOLOv8, YOLOv9, YOLOv10, and YOLOv11 are examined for lung cancer detection and classification. The experimental results indicate that the prediction results of YOLOv8 are better than those of other YOLO versions, with a precision rate of 90.32% and a recall rate of 84.91%, which proves that the model can effectively assist physicians in lung cancer diagnosis and improve the accuracy of disease localization and identification.
Keywords: lung cancer, early diagnosis, object detection, YOLO, Smart medicine
Received: 26 Jan 2025; Accepted: 07 Apr 2025.
Copyright: © 2025 Huang, Chung and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jia-Lang Xu, Chaoyang University of Technology, Taichung, Taiwan
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.