Survey of Bone Fracture Detection Techniques

Jacob, Nathanael E.; Wyawahare, Medha

doi:10.5120/12452-9342

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…SeeBoneAI, a pioneering bone fracture detection system, is underpinned by a novel and unique model based on the cutting-edge CNN methodology with Fuzzy C-means. Employing Convolutional Neural Networks (CNNs), it dissects medical images, notably X-rays and CT scans, to autonomously detect fractures with precision [7,8]. This novel model represents a breakthrough in fracture detection, marking a significant advancement in healthcare technology.…”

Section: Methodsmentioning

confidence: 99%

Enhancing Healthcare Through Seeboneai an Advanced CNN and C-Means Algorithms for Automated Bone Fracture Detection

2023

IRJMETS

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Precision and swift diagnosis are paramount in addressing bone fractures. This study introduces "SeeBoneAI," an innovative system that leverages advanced Convolutional Neural Networks (CNNs) in combination with C-Means algorithms to automate the detection and classification of bone fractures in medical imaging, with a primary focus on X-ray images. SeeBoneAI transcends mere automation; it serves as a dependable tool for healthcare professionals. The system's fundamental components encompass image preprocessing, feature extraction, and classification. The initial preprocessing stage enhances X-ray images, optimizing image quality and reducing noise, laying a robust foundation for subsequent analysis. At the core of SeeBoneAI lies a sophisticated CNN model featuring multiple layers, which autonomously acquire and discern fracture-related patterns. The model training process utilizes a substantial dataset of annotated X-ray images, iteratively refining parameters and minimizing classification errors. After the training phase, the CNN model undergoes rigorous evaluation on a distinct dataset, assessing its performance across a spectrum of metrics, including accuracy and sensitivity. Unlike traditional edge detection techniques, SeeBoneAI's integrated CNN algorithms adeptly navigate multiresolution analysis and effectively mitigate noise interference. Research findings unequivocally affirm SeeBoneAI's proficiency in fracture detection across varying image resolutions and noise profiles, thereby elevating healthcare standards. This innovation stands as a vanguard in the realm of medical image analysis, holding the promise of redefining bone fracture diagnosis and treatment paradigms.

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Section: Methodsmentioning

confidence: 99%

Enhancing Healthcare Through Seeboneai an Advanced CNN and C-Means Algorithms for Automated Bone Fracture Detection

2023

IRJMETS

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“…In general, the Xray is the first screening test performed when the patient arrives at the hospital. Due to the high cost, CT and MRI are indicated only in cases where X-rays do not provide the necessary detail [1]. MRI provides better contrast between cortical bone, bone marrow, muscle, and soft tissues in the body, yet it is the most expensive and slower technique [2].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Bone Fracture Detection Using Microwave Imaging

Santos

Fernandes

Costa

2022

IEEE Open J. Antennas Propag.

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This paper studies the feasibility of Microwave Imaging (MWI) for detection of fractures in superficial bones like the tibia, using a simple and practical setup. First-responders could use it for fast preliminary diagnosis in emergency locations, where X-Rays are not available. It may prove valuable also for cases where X-ray are not recommended, e.g., length pregnant women or children. The method is inspired on the synthetic aperture radar technique. A single Vivaldi antenna is used to linearly scan the bone in the 8.3-11.1 GHz frequency range and collect the scattered fields. The system is operated in air, without the need for impractical impedance-matching immersion liquids. The image is reconstructed using a Kirchhoff migration algorithm. A Singular Value Decomposition (SVD) strategy is used to remove skin and background artifacts. To test this technique, a set of full-wave simulations and experiments were conducted on a multilayer phantom and on an ex-vivo animal bone. Results show that the system can detect and locate bone transverse fractures as small as 1 mm width and 13 mm deep, even when the bone is wrapped by 2 mm thick skin.

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

Joshi

Singh

2020

Artif Intell Rev

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Survey of Bone Fracture Detection Techniques

Cited by 10 publications

References 28 publications

Enhancing Healthcare Through Seeboneai an Advanced CNN and C-Means Algorithms for Automated Bone Fracture Detection

Enhancing Healthcare Through Seeboneai an Advanced CNN and C-Means Algorithms for Automated Bone Fracture Detection

Feasibility of Bone Fracture Detection Using Microwave Imaging

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

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