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

24 Aug 2023  ·  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.

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Datasets


Introduced in the Paper:

GRAZPEDWRI-DX

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Object Detection GRAZPEDWRI-DX DeepLOC mAP 65.4 # 10

Methods