Lower Leg Bone Fracture Detection and Classification Using Faster RCNN for X-Rays Images

Abbas, Waseem; Adnan, Syed Muhammad; Javid, Muhammad Arshad; Majeed, Fahad; Ahsan, Talha; zeb, Haroon; Hassan, Syed Saqlian

doi:10.1109/inmic50486.2020.9318052

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…Ma and Luo introduced a two-stage system of Crack-Sensitive CNN for fracture detection, emphasizing the advantages of multi-stage models [10]. Abbas et al [11] used Faster RCNN for lower leg bone fracture detection, showcasing the utility of object detection methods. Zhang et al [12] introduced a new window loss function for fracture detection and localization, enhancing localization accuracy.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning Empowered Bone Fracture Detection

Baig,

Ul Haq,

Akram

et al. 2024

Decis. Mak. Adv.

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Detection of bone fractures using modern technology has significant implications in medical analysis and artificial intelligence. This importance is especially pronounced in the realm of deep learning. Deep learning techniques find extensive application in the field of medicine and disease classification. The early identification of bone fractures is crucial for efficient treatment planning and patient care. Our research proposes a transfer learning-based model for predicting bone fractures using a dataset of bone X-ray images. These images will be classified into two categories: normal and bone fracture, based on extracted features. Our proposed model, the Bone Fracture Detection Transfer Learning Algorithm (BFDTLA), achieved an average accuracy of 97% on the dataset. The BFDTLA model demonstrated superior performance when compared to previous quantitative and qualitative research studies. This research focuses on the early detection of bone fractures using transfer learning algorithms, emphasizing the significance of accurate and timely diagnosis.

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Section: Related Workmentioning

confidence: 99%

Transfer Learning Empowered Bone Fracture Detection

Baig,

Ul Haq,

Akram

et al. 2024

Decis. Mak. Adv.

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“…In 2020, W. Abbas et al used Faster R-CNN to detect and classify fractures in lower leg X-rays [19], collecting X-ray images of lower leg fractures from 50 patients. The Faster R-CNN model achieved 94% accuracy, sensitivity, and specificity of 96% and 90%, respectively.…”

Section: Fracture Detectionmentioning

confidence: 99%

Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis

Feng,

Lin,

Chiang

et al. 2023

Applied Sciences

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Osteoporosis is a common problem in orthopedic medicine, and it has become an important medical issue in orthopedics as Taiwan is gradually becoming an aging society. In the diagnosis of osteoporosis, the bone mineral density (BMD) derived from dual-energy X-ray absorptiometry (DXA) is the main criterion for orthopedic diagnosis of osteoporosis, but due to the high cost of this equipment and the lower penetration rate of the equipment compared to the X-ray images, the problem of osteoporosis has not been effectively solved for many people who suffer from osteoporosis. At present, in clinical diagnosis, doctors are not yet able to accurately interpret X-ray images for osteoporosis manually and must rely on the data obtained from DXA. In recent years, with the continuous development of artificial intelligence, especially in the fields of machine learning and deep learning, significant progress has been made in image recognition. Therefore, it is worthwhile to revisit the question of whether it is possible to use a convolutional neural network model to read a hip X-ray image and then predict the patient’s BMD. In this study, we proposed a hip X-ray image segmentation model and a hip X-ray image recognition classification model. First, we used the U-Net model as a framework to segment the femoral neck, greater trochanter, Ward’s triangle, and the total hip in the hip X-ray images. We then performed image matting and data augmentation. Finally, we constructed a predictive model for osteoporosis using deep learning algorithms. In the segmentation experiments, we used intersection over union (IoU) as the evaluation metric for image segmentation, and both the U-Net model and the U-Net++ model achieved segmentation results greater than or equal to 0.5. In the classification experiments, using the T-score as the classification basis, the total hip using the DenseNet121 model has the highest accuracy of 74%.

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“…Developments in CT fracture detection have been presented for the rib cage [4], spine [5] and skull [16]. We use Faster-RCNN [20] for fracture localization and classification to extract pelvic ring disruptions from CT scans since it is a well-established architecture for both general purpose object and fracture detection [2,28].…”

Section: Related Workmentioning

confidence: 99%

An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma

Zapaishchykova¹,

Dreizin²,

Li³

et al. 2021

Preprint

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Pelvic ring disruptions result from blunt injury mechanisms and are often found in patients with multi-system trauma. To grade pelvic fracture severity in trauma victims based on whole-body CT, the Tile AO/OTA classification is frequently used. Due to the high volume of whole-body trauma CTs generated in busy trauma centers, an automated approach to Tile classification would provide substantial value, e. g., to prioritize the reading queue of the attending trauma radiologist. In such scenario, an automated method should perform grading based on a transparent process and based on interpretable features to enable interaction with human readers and lower their workload by offering insights from a first automated read of the scan. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grade classification. The method operates similarly to human interpretation of CT scans and first detects distinct pelvic fractures on CT with high specificity using a Faster-RCNN model that are then interpreted using a structural causal model based on clinical best practices to infer an initial Tile grade. The Bayesian causal model and finally, the object detector are then queried for likely co-occurring fractures that may have been rejected initially due to the highly specific operating point of the detector, resulting in an updated list of detected fractures and corresponding final Tile grade. Our method is transparent in that it provides finding location and type using the object detector, as well as information on important counterfactuals that would invalidate the system's recommendation and achieves an AUC of 83.3%/85.1% for translational/rotational instability. Despite being designed for human-machine teaming, our approach does not compromise on performance compared to previous black-box approaches.

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Lower Leg Bone Fracture Detection and Classification Using Faster RCNN for X-Rays Images

Cited by 12 publications

References 13 publications

Transfer Learning Empowered Bone Fracture Detection

Transfer Learning Empowered Bone Fracture Detection

Deep Learning-Based Hip X-ray Image Analysis for Predicting Osteoporosis

An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma

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