DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images
This work addresses a domain-specific problem for radiologists by improving computer-aided diagnosis in wrist X-ray analysis, but it is incremental as it builds on existing methods like YOLO and Swin Transformer.
The paper tackles bone pathology localization and classification in wrist X-ray images by combining YOLO and a modified Swin Transformer, achieving accurate detection and classification of abnormalities.
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.