DeepLOC: Deep Learning-Based Bone Pathology Localization and Classification in Wrist X-Ray Images

Dibo, Razan; Galichin, Andrey; Astashev, Pavel; Dylov, Dmitry V.; Rogov, Oleg Y.

doi:10.1007/978-3-031-54534-4_14

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…The dataset was annotated by a group of paediatric radiologists. There are nine different types of annotation objects and each image can be associated with multiple objects [35][36] .…”

Section: Methods Dataset and Image Pre-processingmentioning

confidence: 99%

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WCAY Object Detection of Fractures for X-ray Images of Multiple Sites

Chen,

Liu,

et al. 2024

Preprint

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Presented herein is the WCAY (Weighted Channel attention YOLO) model, meticulously crafted to identify fracture features across diverse X-ray image sites. This model integrates novel core operators and an innovative attention mechanism to enhance its efficacy. Initially, leveraging the benefits of DSConv (Dynamic Snake Convolution), adept at capturing elongated tubular structural features, we introduce the DSC-C2f module to augment the model's fracture detection performance by replacing a portion of C2f. Subsequently, we integrate the newly proposed Weighted Channel attention (WCA) mechanism into the architecture to bolster feature fusion and improve fracture detection across various sites. Comparative experiments were conducted, evaluating the performance of several attention mechanisms. These enhancement strategies were validated through experimentation on public X-ray image datasets (FracAtlas and GRAZPEDWRI-DX). Multiple experimental comparisons substantiate the model's efficacy, demonstrating its superior accuracy and real-time detection capabilities. According to the experimental findings, on the FracAtlas dataset, our WCAY model exhibits a notable 8.8% improvement in mean Average Precision (mAP) over the original model. On the GRAZPEDWRI-DX dataset, the mAP reaches 64.4%, with a detection accuracy of 93.9% for the "fracture" category alone. The proposed model represents a substantial advancement over the original algorithm when compared to other state-of-the-art object detection models. The code is publicly available at https://github.com/cccp421/Fracture-Detection-WCAY .

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“…The dataset was annotated by a group of paediatric radiologists. There are nine different types of annotation objects and each image can be associated with multiple objects [35][36] .…”

Section: Methods Dataset and Image Pre-processingmentioning

confidence: 99%

“…demonstrated the YOLOv5 model's proficiency in lumbar vertebrae localization, achieving an impressive average accuracy of 0.975. Furthermore, in pediatric wrist fracture detection, Dibo et al [35] . enhanced YOLOv7 with the CBAM attention mechanism, achieving improved performance on the GRAZPEDWRI-DX dataset.…”

Section: Related Workmentioning

confidence: 99%

WCAY Object Detection of Fractures for X-ray Images of Multiple Sites

Chen,

Liu,

et al. 2024

Preprint

View full text Add to dashboard Cite

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WCAY object detection of fractures for X-ray images of multiple sites

Chen,

Liu,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The WCAY (weighted channel attention YOLO) model, which is meticulously crafted to identify fracture features across diverse X-ray image sites, is presented herein. This model integrates novel core operators and an innovative attention mechanism to enhance its efficacy. Initially, leveraging the benefits of dynamic snake convolution (DSConv), which is adept at capturing elongated tubular structural features, we introduce the DSC-C2f module to augment the model’s fracture detection performance by replacing a portion of C2f. Subsequently, we integrate the newly proposed weighted channel attention (WCA) mechanism into the architecture to bolster feature fusion and improve fracture detection across various sites. Comparative experiments were conducted, to evaluate the performances of several attention mechanisms. These enhancement strategies were validated through experimentation on public X-ray image datasets (FracAtlas and GRAZPEDWRI-DX). Multiple experimental comparisons substantiated the model’s efficacy, demonstrating its superior accuracy and real-time detection capabilities. According to the experimental findings, on the FracAtlas dataset, our WCAY model exhibits a notable 8.8% improvement in mean average precision (mAP) over the original model. On the GRAZPEDWRI-DX dataset, the mAP reaches 64.4%, with a detection accuracy of 93.9% for the “fracture” category alone. The proposed model represents a substantial improvement over the original algorithm compared to other state-of-the-art object detection models. The code is publicly available at https://github.com/cccp421/Fracture-Detection-WCAY . Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-77878-6.

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DeepLOC: Deep Learning-Based Bone Pathology Localization and Classification in Wrist X-Ray Images

Cited by 2 publications

References 26 publications

WCAY Object Detection of Fractures for X-ray Images of Multiple Sites

WCAY Object Detection of Fractures for X-ray Images of Multiple Sites

WCAY object detection of fractures for X-ray images of multiple sites

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