In recent years, computer-aided diagnosis systems have shown great potential in assisting radiologists with accurate and efficient medical image analysis. This paper presents a novel approach for bone pathology localization and classification in wrist X-ray images using a combination of YOLO (You Only Look Once) and the Shifted Window Transformer (Swin) with a newly proposed block. The proposed methodology addresses two critical challenges in wrist X-ray analysis: accurate localization of bone pathologies and precise classification of abnormalities. The YOLO framework is employed to detect and localize bone pathologies, leveraging its real-time object detection capabilities. Additionally, the Swin, a transformer-based module, is utilized to extract contextual information from the localized regions of interest (ROIs) for accurate classification.
@article{arxiv.2308.12727,
title = {DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images},
author = {Razan Dibo and Andrey Galichin and Pavel Astashev and Dmitry V. Dylov and Oleg Y. Rogov},
journal= {arXiv preprint arXiv:2308.12727},
year = {2023}
}