Feasibility of Bone Fracture Detection Using Microwave Imaging

Santos, Kesia C.; Fernandes, Carlos A.; Costa, Jorge R.

doi:10.1109/ojap.2022.3194217

Cited by 22 publications

(23 citation statements)

References 36 publications

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“…Therefore, to have a higher potential resolution to resolve smaller fractures, the transition toward a higher microwave frequency is needed. SANTOS et al 13 proposed a microwave imaging technique for the diagnosis of fractures in superficial bones. The experimental results demonstrate that at the 8.3–11.1 GHz frequency range, transverse fractures as small as 1 mm can be detected.…”

Section: Discussionmentioning

confidence: 99%

Microwave bone fracture diagnosis using deep neural network

Beyraghi,

Ghorbani,

Shabanpour

et al. 2023

Sci Rep

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This paper studies the feasibility of a deep neural network (DNN) approach for bone fracture diagnosis based on the non-invasive propagation of radio frequency waves. In contrast to previous “semi-automated” techniques, where X-ray images were used as the network input, in this work, we use S-parameters profiles for DNN training to avoid labeling and data collection problems. Our designed network can simultaneously classify different complex fracture types (normal, transverse, oblique, and comminuted) and estimate the length of the cracks. The proposed system can be used as a portable device in ambulances, retirement houses, and low-income settings for fast preliminary diagnosis in emergency locations when expert radiologists are not available. Using accurate modeling of the human body as well as changing tissue diameters to emulate various anatomical regions, we have created our datasets. Our numerical results show that our design DNN is successfully trained without overfitting. Finally, for the validation of the numerical results, different sets of experiments have been done on the sheep femur bones covered by the liquid phantom. Experimental results demonstrate that fracture types can be correctly classified without using potentially harmful and ionizing X-rays.

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Section: Discussionmentioning

confidence: 99%

Microwave bone fracture diagnosis using deep neural network

Beyraghi,

Ghorbani,

Shabanpour

et al. 2023

Sci Rep

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“…Samothai et al [4] explored the use of you only look once (YOLO) series models for real-time detection in clinical applications. Santos et al [5] took an innovative approach by investigating bone fracture detection using microwave imaging. In the context of artificial intelligence, AlGhaithi & al Maskari [6] discussed the application of AI in bone fracture detection, emphasizing its potential to enhance diagnostic precision.…”

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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“…K. C. Santos et al [10] wanted to develop a model that can even detect transverse fractures as thin as 1mm to 13mm deep. They used the concept of feasibility for the model implementation.…”

Section: Related Workmentioning

confidence: 99%

“…They used the concept of feasibility for the model implementation. [10] emphasizes the importance of using the Vivaldi antenna for the accurate detection of bone fractures.…”

Section: Related Workmentioning

confidence: 99%

Detection of Hand Bone Fractures in X-Ray Images Using Hybrid YOLO NAS

Medaramatla,

Samhitha,

Pande

et al. 2024

IEEE Access

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The majority of bones that have fractured in humans are hand bones. As we use our hands widely, they need early and accurate detection to be diagnosed. Fractures in the hands are most frequently brought on by blunt force trauma, sports injuries, and bone fragility. Getting an X-ray of the affected area of the bone and then discussing the results with a medical practitioner or radiologist is the standard procedure for determining whether or not a fracture exists in the bone. The majority of medical professionals and radiologists use X-rays to diagnose hand fractures; however, in some instances, they might miss small or hairline fractures. Additionally, it might be difficult to find a good radiologist who can detect the fracture properly and in time, because a delay in diagnosis can cause the injury to be more severe, and the bone might not be recovered properly. Therefore, to detect hand bone and joint fractures through X-rays, a hybrid model was developed that uses deep learning algorithms YOLO NAS, Efficient Det, and DETR3, which are widely recognized for their exact object detection capabilities. The dataset used for this model is a hybrid dataset of 4736 hand-bone X-ray images, they were further classified into 6 classes based on their types. To evaluate the performance the best method is to compare the proposed model with the existing models, hence, the model was compared with various existing algorithms and result analysis was done.

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Feasibility of Bone Fracture Detection Using Microwave Imaging

Cited by 22 publications

References 36 publications

Microwave bone fracture diagnosis using deep neural network

Microwave bone fracture diagnosis using deep neural network

Transfer Learning Empowered Bone Fracture Detection

Detection of Hand Bone Fractures in X-Ray Images Using Hybrid YOLO NAS

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