Automatic X-ray Image Classification System

Basha, Cmak Zeelan; Padmaja, T. Maruthi; Balaji, G.

doi:10.1007/978-981-10-5547-8_5

Cited by 25 publications

(3 citation statements)

References 6 publications

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“…Guo et al [ 33 ] collected CT images of orbital blowout fractures from the Shanghai Ninth People’s Hospital and used the Inception-V3 convolutional neural network (CNN) framework with the XGBoost model to classify the orbital blowout fractures. Zeelan et al [ 34 ] detected bone fracture from X-ray images using the machine learning models probabilistic neural network (PNN), backpropagation neural network (BPNN), and support vector machine (SVM) and classified the input images into the classes skull, head, chest, hand, and spine. A deep convolutional neural network (DCNN) model developed by Cheng et al [ 35 ] not only detected hip fractures on plain frontal pelvic radiographs (PXRs) with a good accuracy rate, but it was also good at localizing fracture areas.…”

Section: Related Workmentioning

confidence: 99%

A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture

Rashid

Zia

Rehman

et al. 2023

Life

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The emergency department of hospitals receives a massive number of patients with wrist fracture. For the clinical diagnosis of a suspected fracture, X-ray imaging is the major screening tool. A wrist fracture is a significant global health concern for children, adolescents, and the elderly. A missed diagnosis of wrist fracture on medical imaging can have significant consequences for patients, resulting in delayed treatment and poor functional recovery. Therefore, an intelligent method is needed in the medical department to precisely diagnose wrist fracture via an automated diagnosing tool by considering it a second option for doctors. In this research, a fused model of the deep learning method, a convolutional neural network (CNN), and long short-term memory (LSTM) is proposed to detect wrist fractures from X-ray images. It gives a second option to doctors to diagnose wrist facture using the computer vision method to lessen the number of missed fractures. The dataset acquired from Mendeley comprises 192 wrist X-ray images. In this framework, image pre-processing is applied, then the data augmentation approach is used to solve the class imbalance problem by generating rotated oversamples of images for minority classes during the training process, and pre-processed images and augmented normalized images are fed into a 28-layer dilated CNN (DCNN) to extract deep valuable features. Deep features are then fed to the proposed LSTM network to distinguish wrist fractures from normal ones. The experimental results of the DCNN-LSTM with and without augmentation is compared with other deep learning models. The proposed work is also compared to existing algorithms in terms of accuracy, sensitivity, specificity, precision, the F1-score, and kappa. The results show that the DCNN-LSTM fusion achieves higher accuracy and has high potential for medical applications to use as a second option.

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Section: Related Workmentioning

confidence: 99%

A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture

Rashid

Zia

Rehman

et al. 2023

Life

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“…With the development of artificial intelligence, computer vision technology, a science of making artificial systems perceive the world from images or multi-dimensional data, is widely used in image classification, object detection, image segmentation, and other tasks.Therefore,using computer vision technology to assist radiology detection has become an essential means of modern imaging medicine, effectively reducing the diagnosis time, diagnosis workload, and the severe consequences caused by potential manual diagnosis errors. [5][6][7][8][9] Due to its excellent imaging performance and simpler background noise compared with computed tomography (CT) or magnetic resonance imaging (MRI), Xrays can significantly improve image processing speed and machine learning reasoning. Therefore, using the artificial intelligence technology to assist X-ray detection has good accuracy and efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…With the development of artificial intelligence, computer vision technology, a science of making artificial systems perceive the world from images or multi‐dimensional data, is widely used in image classification, object detection, image segmentation, and other tasks. Therefore, using computer vision technology to assist radiology detection has become an essential means of modern imaging medicine, effectively reducing the diagnosis time, diagnosis workload, and the severe consequences caused by potential manual diagnosis errors 5–9 …”

Section: Introductionmentioning

confidence: 99%

Enhanced deep residual network for bone classification and abnormality detection

Guo

2022

Medical Physics

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Purpose A two‐stage deep learning framework for bone classification and abnormality detection is proposed based on X‐rays. The primary focus is on improving the speed of orthopedic disease diagnosis and helping physicians reduce the probability of false diagnoses. Methods The method is based on two stages. In the first stage, one classifier with ResNeXt50 as the backbone is used to classify bones to eliminate the effect of bone type differences on abnormality detection. In the second stage, seven anomaly detectors are trained based on each type of training data. The seven detectors tested the seven results of the first stage, respectively. Pretrained models, data augmentation, focal loss, label smoothing loss, LR‐attenuation and early stopping are used to improve performance and reduce the risk of overfitting. Results Experiments are based on the largest dataset for bone abnormality detection, MURA. In the first stage for bone classification, we got an accuracy of 96.69%, a sensitivity of 96.69%, a specificity of 99.46%, and an F1 score of 96.42%. In the second stage for abnormality detection, we got an accuracy of 84.15%, a sensitivity of 84.15%, a specificity of 87.50%, an F1 score of 84.10%, a Cohen's Kappa of 0.72, and an area under the ROC curve (AUC) score of 0.90. Conclusions Compared with other excellent convolutional neural network models, the framework's effectiveness was verified with better accuracy, sensitivity, specificity, F1 score, Cohen's Kappa score, and AUC score for bone classification and abnormality detection.

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An Assessment on Bone Cancer Detection Using Various Techniques in Image Processing

Anand

Arulselvi

Balaji³

2022

Applications of Computational Methods in Manufacturing and Product Design

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Automatic X-ray Image Classification System

Cited by 25 publications

References 6 publications

A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture

A Minority Class Balanced Approach Using the DCNN-LSTM Method to Detect Human Wrist Fracture

Enhanced deep residual network for bone classification and abnormality detection

An Assessment on Bone Cancer Detection Using Various Techniques in Image Processing

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