A survey of fracture detection techniques in bone X-ray images

Joshi, Deepa; Singh, Thipendra P.

doi:10.1007/s10462-019-09799-0

Cited by 50 publications

(23 citation statements)

References 67 publications

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“…Joshi et al [ 31 ] reviewed several contributions that apply artificial intelligence techniques to fracture detection. Among techniques, they pointed out that only a few works use CNNs for fracture detection.…”

Section: Introductionmentioning

confidence: 99%

Comparing Stacking Ensemble Techniques to Improve Musculoskeletal Fracture Image Classification

2021

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Bone fractures are among the main reasons for emergency room admittance and require a rapid response from doctors. Bone fractures can be severe and can lead to permanent disability if not treated correctly and rapidly. Using X-ray imaging in the emergency room to detect fractures is a challenging task that requires an experienced radiologist, a specialist who is not always available. The availability of an automatic tool for image classification can provide a second opinion for doctors operating in the emergency room and reduce the error rate in diagnosis. This study aims to increase the existing state-of-the-art convolutional neural networks’ performance by using various ensemble techniques. In this approach, different CNNs (Convolutional Neural Networks) are used to classify the images; rather than choosing the best one, a stacking ensemble provides a more reliable and robust classifier. The ensemble model outperforms the results of individual CNNs by an average of 10%.

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Section: Introductionmentioning

confidence: 99%

Comparing Stacking Ensemble Techniques to Improve Musculoskeletal Fracture Image Classification

2021

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“…A collection of research and methods on CAD in medical images can be found in [ 8 , 22 ]. Compared to these developments, techniques for automatic diagnosing bone fractures are scarce [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Automatic detection and classification of peri-prosthetic femur fracture

Alzaid

Wignall²,

Dogramadzi

et al. 2022

Int J CARS

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Purpose Object classification and localization is a key task of computer-aided diagnosis (CAD) tool. Although there have been numerous generic deep learning (DL) models developed for CAD, there is no work in the literature to evaluate their effectiveness when utilized in diagnosing fractures in proximity of joint implants. In this work, we aim to assess the performance of existing classification systems on binary and multi-class problems (fracture types) using plain radiographs. In addition, we evaluated the performance of object detection systems using the one- and two-stage DL architectures. Methods A data set of 1272 X-ray images of Peri-prosthetic Femur Fracture PFF was collected. The fractures were annotated with bounding boxes and classified according to the Vancouver Classification System (type A, B, C) by two clinical specialists. Four classification models such as Densenet161, Resnet50, Inception, VGG and two object detection models such as Faster RCNN and RetinaNet were evaluated, and their performance compared. Six confusion matrix-based measures were reported to evaluate fracture classification. For localization of the fracture, Average Precision and localization accuracy were reported. Results The Resnet50 showed the best performance with $$95\%$$ 95 % accuracy and $$94\%$$ 94 % F1-score in the binary classification: fracture/normal. In addition, the Resnet50 showed $$90\%$$ 90 % accuracy in multi-classification (normal, Vancouver type A, B and C). Conclusions A large data set of PFF images and the annotations of fracture features by two independent assessments were created to implement a DL-based approach for detecting, classifying and localizing PFFs. It was shown that this approach could be a promising diagnostic tool of fractures in proximity of joint implants.

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“…It provides an easy and quick examination through the use of a very small dose of ionizing radiation to produce pictures of any tooth or bone in the body especially in cases of fractured joints and bones [17] .…”

Section: Bones and Teeth Diagnosis By X-raymentioning

confidence: 99%

Manipulation of different phytochemical classes against inflammation induced by radiations

Batanony

El-Feky

2021

Egypt. J. Chem.

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This review represents a scientific approach regarding the utilization of phytochemicals in research work field as promising radioprotective agents. Recently, numerous studies proved the efficacy of phytochemicals as antioxidant, antimicrobial, anti-inflammatory and anticancer. Consequently, based on the previous exerted activities, phytochemicals have drawn the attention as radioprotective agents in both prophylactic and post radiation treatment protocols. Moreover, this collective work is basically concerning about the radiation meaning, types, effect on human health and offers a new perspective scenario about the outstanding benefits of phytochemicals, found in many plants around us and their unique radioprotective effect.

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A survey of fracture detection techniques in bone X-ray images

Cited by 50 publications

References 67 publications

Comparing Stacking Ensemble Techniques to Improve Musculoskeletal Fracture Image Classification

Comparing Stacking Ensemble Techniques to Improve Musculoskeletal Fracture Image Classification

Automatic detection and classification of peri-prosthetic femur fracture

Manipulation of different phytochemical classes against inflammation induced by radiations

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