@inbook{de57cb8b42fe4c3492340a304ced69c6,
title = "YOLO-LXR: An Enhanced Model for Pathology Detection in Chest X-Rays",
abstract = "Algorithms for object detection, particularly those based on YOLO, have demonstrated impressive effectiveness in achieving a balance between accuracy and speed. Their use in medical imaging has shown promise, but remains underexplored. In this study, a YOLO-like model is proposed that focuses on detecting lung pathologies in X-ray images, is based on YOLOv5, and uses Squeeze-and-Excite and Residual Blocks. It outperforms most YOLO and YOLO-like architectures in terms of accuracy, and compared to YOLOv5, experiments showed an increase in recall and mAP@50 of 3.3\% and 1.5\%, respectively. The suggested architecture yields encouraging results, establishing itself as a cutting-edge solution for accurate and efficient detection of pathologies in chest X-rays. The code implementation of this project is publicly available at: https://github.com/GKatsagannis/YOLO-LXR.",
keywords = "Algorithms, Humans, Radiographic Image Interpretation, Computer-Assisted - methods, Radiography, Thoracic - methods, Residual Block, Squeeze-and-Excitation Networks, X-Ray, YOLO",
author = "Gerasimos Katsagannis and Bentley, \{Barry L.\}",
year = "2026",
month = may,
day = "21",
doi = "10.3233/SHTI260099",
language = "English",
volume = "336",
series = "Studies in health technology and informatics",
publisher = "IOS Press",
pages = "12--16",
booktitle = "Opening the Personal Gate between Technology and Health Care",
note = "MIE 2026, the 36th Medical Informatics Europe Conference ; Conference date: 25-05-2026 Through 28-05-2026",
}