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

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

URL: http://arxiv.org/abs/2308.12727v1
Date: Thu, 24 Aug 2023 12:06:10 GMT
Title: DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images
Authors: Razan Dibo and Andrey Galichin and Pavel Astashev and Dmitry V. Dylov and Oleg Y. Rogov
Abstract summary: This paper presents a novel approach for bone pathology localization and classification in wrist X-ray images. The proposed methodology addresses two critical challenges in wrist X-ray analysis: accurate localization of bone pathologies and precise classification of abnormalities.
Score: 1.45543311565555
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
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.

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