Bone X-Rays Classification and Abnormality Detection using Xception Network

El-Saadawy, Hadeer; Tantawi, Manal M.; Shedeed, Howida A.; Tolba, Mohamed F.

doi:10.21608/ijicis.2021.79392.1101

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Convolutional neural networks (CNNs) tend to be a more efficient strategic method for feature detection and have rapidly acquired significance in the computer vision area in the past years. To modify clinical challenges, CNNs “train” distinguishing patterns using models, such as Inception V3 [ 18 ], ResNet [ 19 ], U-Net [ 20 ], Xception [ 21 ], and DenseNet [ 22 ]. Due to limited annotated data availability, data augmentation and other data generative methods can be used to increase the size of the dataset.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…study's Various network architectures have been successful for every purpose (12) . A two-stage classification approach is suggested by Hadeer El-Saadawyv (13) for the identification of bone abnormalities and bone type identification. For all experiments, the only exception well before system is taken into account.…”

Section: Introductionmentioning

confidence: 99%

Classification of Long-Bone Fractures Using Modified Faster RCNN for X-Ray Images

Vironicka¹,

Sathiaseelan²

2023

IJST

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Objectives: Methods in machine learning have been shown to be an essential tool for the diagnosis and treatment of disease. The scientists are constantly looking for new technology that might improve current clinical practise. One of the most well-liked study fields continues to be computerized bone fracture identification and categorization. Furthermore, identifying cracks and determining where they occur have been challenges for historically developed approaches for the long bone fracture method. Methods: We propose a new approach for automatic fracture detection in X-Ray images. This approach is built on top of two-stage fracture detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The described procedure is divided into four main steps: X-ray images are used to: I identify the bone contour; (ii) identify fracture-points or fractures; (iii) find a similar set of shapes that are compatible with the identification of cracks; and (iv) classify and thoroughly evaluate the fracture-type using bounding box. Finding: The resolution process uses the stretched numerical conventional line techniques (RDS), arcs, discrete curvature, and shape directory, among other mathematical characteristics of digital curves. We assessed the suggested model's performance in terms of classification and detection. We divide x-ray pictures of bone fractures into 2 groups, fracture and non-fracture, and we also use a rectangular box to identify the location of fractures. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. Novelty: This study develops a new approach to automatically and accurately detect fractures in X-Ray images. The proposed approach is developed on the basis of Faster R-CNN algorithm with a major modification for the task of rotated bounding box prediction.

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Bone X-Rays Classification and Abnormality Detection using Xception Network

Cited by 2 publications

References 7 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

Classification of Long-Bone Fractures Using Modified Faster RCNN for X-Ray Images

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