IBFDS: Intelligent bone fracture detection system

Dimililer, Kamil

doi:10.1016/j.procs.2017.11.237

Cited by 63 publications

(31 citation statements)

References 18 publications

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“…Once the correct classification system is defined, an adequate dataset is certainly one of the most important aspects for a deep learning-based application to operate efficiently. Even if, in some studies, good results have been obtained for the fracture/no-fracture classification without using a large dataset [9,19], a correct number of images is suggested when the network has to distinguish between different sub-groups of fractures. The dataset could be increased and balanced with data augmentation techniques, if needed, but without adding useless or misleading information.…”

Section: Discussionmentioning

confidence: 99%

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X-Ray Bone Fracture Classification Using Deep Learning: A Baseline for Designing a Reliable Approach

et al. 2020

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In recent years, bone fracture detection and classification has been a widely discussed topic and many researchers have proposed different methods to tackle this problem. Despite this, a universal approach able to classify all the fractures in the human body has not yet been defined. We aim to analyze and evaluate a selection of papers, chosen according to their representative approach, where the authors applied different deep learning techniques to classify bone fractures, in order to select the strengths of each of them and try to delineate a generalized strategy. Each study is summarized and evaluated using a radar graph with six values: area under the curve (AUC), test accuracy, sensitivity, specificity, dataset size and labelling reliability. Plus, we defined the key points which should be taken into account when trying to accomplish this purpose and we compared each study with our baseline. In recent years, deep learning and, in particular, the convolution neural network (CNN), has achieved results comparable to those of humans in bone fracture classification. Adopting a correct generalization, we are reasonably sure that a computer-aided diagnosis (CAD) system, correctly designed to assist doctors, would save a considerable amount of time and would limit the number of wrong diagnoses.

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Section: Discussionmentioning

confidence: 99%

“…This subsection focuses on the work of Dimililer [9]. Aim: The final aim of this paper was to classify whether a bone in an X-ray image is fractured or not.…”

Section: Intelligent Bone Fracture Detection System (Ibfds)mentioning

confidence: 99%

X-Ray Bone Fracture Classification Using Deep Learning: A Baseline for Designing a Reliable Approach

et al. 2020

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“…Dimililer [4] classified pest insects, and Khashman et al [29] solved the classification problems of 2 Euro and 1 TL coins in slot machines using BPNN. Beside these applications, image compression [30], microRNA analysis [31] and medical applications [32] Tab. I Hidden neuron numbers in recent applications.…”

Section: Hidden Neuron Usage In the Literaturementioning

confidence: 99%

Review and analysis of hidden neuron number effect of shallow backpropagation neural networks

Şekeroğlu¹,

Dimililer²

2020

NNW

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Shallow neural network implementations are still popular for real-life classification problems that require rapid achievements with limited data. Parameters selection such as hidden neuron number, learning rate and momentum factor of neural networks are the main challenges that causes time loss during these implementations. In these parameters, the determination of hidden neuron numbers is the main drawback that affects both training and generalization phases of any neural system for learning efficiency and system accuracy. In this study, several experiments are performed in order to observe the effect of hidden neuron number of 3-layered backpropagation neural network on the generalization rate of classification problems using both numerical datasets and image databases. Experiments are performed by considering the increasing number of total processing elements, and various numbers of hidden neurons are used during the training. The results of each hidden neuron number are analyzed according to the accuracy rates and iteration numbers during the convergence. Results show that the effect of the hidden neuron numbers mainly depends on the number of training patterns. Also obtained results suggest intervals of hidden neuron numbers for different number of total processing elements and training patterns.

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“…surface example during the test or examination [14]. This is finished by examining the relocation of the examples inside discretized subsets or aspect components of the entire picture.…”

Section: Bull Sci Res 1-7 |mentioning

confidence: 99%

A Comprehensive Survey on Crack detection of Bone using various techniques

S¹,

Akshaya²,

Kaviya³

et al. 2020

Bull. Sci. Res.

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Digital image processing plays a key role in manipulation of image and extracting the maximum amount of data from image with help of various algorithm. Digital image correlation algorithm determines the displacement and deformation of pattern across several images. Creating innovation are developing every day in various fields, particularly in restoration condition. Notwithstanding, still some old strategies are very famous. X-ray or CT images are one among the system for identification of bone cracks. during this article, we offer a comprehensive overview of various algorithm and techniques of displacement measurement generally and crack detection especially using digital image processing. we've been successful in highlighting each and each key feature and aspect of crack detection in bone which can take the add this domain further

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IBFDS: Intelligent bone fracture detection system

Cited by 63 publications

References 18 publications

X-Ray Bone Fracture Classification Using Deep Learning: A Baseline for Designing a Reliable Approach

X-Ray Bone Fracture Classification Using Deep Learning: A Baseline for Designing a Reliable Approach

Review and analysis of hidden neuron number effect of shallow backpropagation neural networks

A Comprehensive Survey on Crack detection of Bone using various techniques

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