DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images

TL;DR

This paper introduces DeepLOC, a novel deep learning framework combining YOLO and Swin transformer to improve localization and classification of bone pathologies in wrist X-ray images, enhancing diagnostic accuracy.

Contribution

It presents a new integrated approach using YOLO and Swin transformer for simultaneous localization and classification of wrist bone pathologies.

Findings

Effective localization of bone abnormalities achieved

Accurate classification of wrist pathologies demonstrated

Real-time detection capability confirmed

Abstract

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.