DeepLOC: Deep Learning-based Bone Pathology Localization and
  Classification in Wrist X-ray Images
Razan Dibo Andrey Galichin Pavel Astashev Dmitry V. Dylov Oleg Y. Rogov
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.