Bone X-Rays Classification and Abnormality Detection

Tantawi, Manal M.; Thabet, Rezq; Sayed, Ahmad M.; El-emam, Omer; bake, Gaber Abd El

doi:10.1007/978-981-15-3075-3_20

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…A two-stage classification method was proposed by M. Tantawi, et al [17] to detect the abnormality in the seven extremity upper bones (shoulder, humerus, forearm, elbow, wrist, hand, and finger) using deep learning models. Enhanced X-ray image was fed into two stage classification method to detect bone type and abnormality in the bone.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Bone X-Rays Classification and Abnormality Detection using Xception Network

El-Saadawy

Tantawi

Shedeed

et al. 2021

IJICIS

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Computer aided diagnosis (CAD) has a vital role and becomes an urgent demand nowadays. Bone fractures cases are considered from the most frequently occured dieases among individuals. Moreover, the incorrect diagnosis of the bone fractures cases may cause disability for the patient. Hence, CAD system for bone fractures has become a must. This paper proposes a two-stage classifcation method for bone type classification and bone abnormality detection. Xception pre-trained model is considered for all experiments. Two different approaches are utilized for the testing phase: 1) Singl-view and 2) Multi-view approachs. The enhanced images are fed into the first stage to be classified into one of the seven classes: shoulder, humerus, forearm, elbow, wrist, hand and finger. Thereafter, the classified bones are fed into the second stage to detect whether the bone is normal or International Journal of Intelligent Computing and Information Sciences https://ijicis.journals.ekb.eg/ 83 H. El-Saadawy et al. abnormal. MURA dataset has been utilized for all experiments. Moreover, the last layer of the utilized model is replaced by Support Vector Machine (SVM) layer. The results reveal the superiority of the SVM layer.

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Section: Deep Learning Modelsmentioning

confidence: 99%

Bone X-Rays Classification and Abnormality Detection using Xception Network

El-Saadawy

Tantawi

Shedeed

et al. 2021

IJICIS

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“…Researchers have proposed deep learning-based approaches to address the issues of efficient and effective musculoskeletal abnormality detection (Saadawy et al, 2020;Aal et al, 2018;Yang et al, 2019;Chada, 2019;Goyal et al, 2020;Solovyov & Solovyov, 2020;Irmakci et al, 2019;Rajpurkar et al, 2017;Saif et al, 2019;Tantawi et al, 2020). Those approaches process bone radiographs in detecting musculoskeletal abnormality.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning-based abnormality diagnosis in wrist skeleton radiographs using densenet variants voting

Khan¹,

Arshad²,

Zulfiqar³

et al. 2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Almost one out of five people, including children, suffers from musculoskeletal disorders. It is the second leading cause of disability worldwide. It affects the musculoskeletal system’s major areas, represented by the shoulder, forearm, and wrist. It causes severe pain, joint noises, and disability. To detect the abnormality, the radiologist analyzes the patient’s anatomy through X-rays of different views and projections. To automatically diagnose the abnormality in the musculoskeletal system is a challenging task. Previously, various researchers detected the abnormality in the musculoskeletal system from radiographic images by using several deep learning techniques. They used a capsule network, 169-layer convolutional neural network, and group normalized convolutional neural network in musculoskeletal abnormality detection. However, to propose methods for improving abnormality detection, further work needs to be done because the accuracy of the conventional methods is far away from 90%. This paper presents an ensemble learning-based classification system for detecting abnormality in wrist radiographs. Tags in radiographs may result in learning noisy features hence reducing the performance. Therefore, tags are segmented and removed using UNet trained on the annotated ground truths. Segmented images are then used for voting-based diagnosis. The simulation results show that the proposed methodology improves testing accuracy by 1.5%-4.5% compared to the available wrist abnormality detection methods. The proposed methodology can be used for any kind of musculoskeletal abnormality detection.

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“…However, clinicians' performance is somewhat affected by their physical and psychological status [4], resulting in incorrect diagnoses. Computer-aided diagnosis (CAD) can assist clinicians by reducing time, effort, and human error and provide rapid, accurate diagnoses [5] [6] [7] [8] [9]. X-rays are the main diagnostic tool used by clinicians for bone abnormality detection [10] [11] [12].…”

Section: Introductionmentioning

confidence: 99%

“…Progress in image processing and machine learning techniques has facilitated the emergence of X-ray-based CAD systems that can assist in diagnosing musculoskeletal conditions with promising levels of accuracy [3] [4] [7] [9] [13] [14] [15] [16] [17] [18]. In recent years, many studies [3] [4] [7] [9] [13] [14] [15] [16] [17] [18] have been published regarding bone abnormality detection. However, most consider only one type of bone, which is inconvenient for the real-life clinical setting.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a novel, two-stage, hybrid classification method based on the combination of unsupervised and supervised networks with minimum depth is developed to classify and detect abnormality in the bones of the upper extremity areas (shoulder, humerus, forearm, elbow, wrist, hand, and finger). Hence, this study considers seven bones instead of only one bone as in the literature [3] [4] [7] [9] [13] [14] [15] [16] [17] [18]. To resolve the effect of bone shape variations on the accuracy of abnormality detection, a two-stage architecture is proposed.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Two-Stage GNG–Modified VGG Method for Bone X-Rays Classification and Abnormality Detection

et al. 2021

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This paper introduces a novel, reliable, hybrid, two-stage method for bone x-ray classification and abnormality detection. The proposed method considers bones from seven areas of the upper extremities: namely, shoulder, humerus, forearm, elbow, wrist, hand, and finger. The novelty of the proposed work is not only in the application, but also in the proposed method itself by combining GNG network and eight models built from scratch and inspired from VGG model to achieve best performance and least computations possible. The features extracted from GNG are fed into a two-stage classification step. The first stage classifies a bone X-ray into one of seven types, after which it is directed according to type to one of seven classifiers trained to detect bone abnormality. Hence, the classification step consists of eight different models: one for classification and seven for abnormality detection. Experiments have been carried out using the MURA database, the largest public dataset of bone x-ray images. The best average sensitivity and specificity obtained for the first stage is 95.86% and 99.63%, respectively. For the second stage, the best average sensitivity and specificity obtained is 92.50% and 92.12%, respectively. These results are superior compared to state of art pretrained models. In addition, the computation and processing time are significantly decreased by the proposed scheme. Furthermore, to the best knowledge of authors, the proposed method is the first to integrate seven bones together in the same scheme. Finally, the hierarchical nature of the proposed method allows considering two problems together: bone classification and abnormality detection.

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Bone X-Rays Classification and Abnormality Detection

Cited by 6 publications

References 9 publications

Bone X-Rays Classification and Abnormality Detection using Xception Network

Bone X-Rays Classification and Abnormality Detection using Xception Network

Ensemble learning-based abnormality diagnosis in wrist skeleton radiographs using densenet variants voting

A Hybrid Two-Stage GNG–Modified VGG Method for Bone X-Rays Classification and Abnormality Detection

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