Osteoarthritis detection by applying quadtree analysis to human joint knee X-ray imagery

Chan, Sophal; Dittakan, Kwankamon; Salhi, Subhieh El

doi:10.1080/1206212x.2020.1838145

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…The model reached an accuracy of 70.23% and F1 scores of 67.55%. Sophal et al [17] created a model to select ROI from knee X-rays and extract and classify shape features to distinguish between osteoarthritis images and their severity. The ROI was selected by using Otsu's method from X-rays.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted

et al. 2023

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Knee osteoarthritis (KOA) is a chronic disease that impedes movement, especially in the elderly, affecting more than 5% of people worldwide. KOA goes through many stages, from the mild grade that can be treated to the severe grade in which the knee must be replaced. Therefore, early diagnosis of KOA is essential to avoid its development to the advanced stages. X-rays are one of the vital techniques for the early detection of knee infections, which requires highly experienced doctors and radiologists to distinguish Kellgren-Lawrence (KL) grading. Thus, artificial intelligence techniques solve the shortcomings of manual diagnosis. This study developed three methodologies for the X-ray analysis of both the Osteoporosis Initiative (OAI) and Rani Channamma University (RCU) datasets for diagnosing KOA and discrimination between KL grades. In all methodologies, the Principal Component Analysis (PCA) algorithm was applied after the CNN models to delete the unimportant and redundant features and keep the essential features. The first methodology for analyzing x-rays and diagnosing the degree of knee inflammation uses the VGG-19 -FFNN and ResNet-101 -FFNN systems. The second methodology of X-ray analysis and diagnosis of KOA grade by Feed Forward Neural Network (FFNN) is based on the combined features of VGG-19 and ResNet-101 before and after PCA. The third methodology for X-ray analysis and diagnosis of KOA grade by FFNN is based on the fusion features of VGG-19 and handcrafted features, and fusion features of ResNet-101 and handcrafted features. For an OAI dataset with fusion features of VGG-19 and handcrafted features, FFNN obtained an AUC of 99.25%, an accuracy of 99.1%, a sensitivity of 98.81%, a specificity of 100%, and a precision of 98.24%. For the RCU dataset with the fusion features of VGG-19 and the handcrafted features, FFNN obtained an AUC of 99.07%, an accuracy of 98.20%, a sensitivity of 98.16%, a specificity of 99.73%, and a precision of 98.08%.

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Section: Related Workmentioning

confidence: 99%

Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted

et al. 2023

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“…Convolutional neural networks (CNNs) are a type of deep learning technology for working with images that can eliminate the need for handmade feature extractors [20]. CNNs have been employed for various image classification applications, with CNN architectures for medical image analysis being developed in recent studies [21].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Detection and Classification of The Osteoarthritis in Knee Joint Using Transfer Learning with Convolutional Neural Networks (CNNs)

Ahmed

Mohammed

2022

Iraqi Journal of Science

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Osteoarthritis (OA) is a disease of human joints, especially the knee joint, due to significant weight of the body. This disease leads to rupture and degeneration of parts of the cartilage in the knee joint, which causes severe pain. Diagnosis of this disease can be obtained through X-ray. Deep learning has become a popular solution to medical issues due to its fast progress in recent years. This research aims to design and build a classification system to minimize the burden on doctors and help radiologists to assess the severity of the pain, enable them to make an optimal diagnosis and describe the correct treatment. Deep learning-based approaches, such as Convolution Neural Networks (CNNs), have been used to detect knee OA using transfer learning with fine-tuning. This paper proposed three versions of pre-trained networks (VGG16, VGG19, and ResNet50) for handling the classification task. According to the classification results, The proposed model ResNet50 outperformed the other models a validation accuracy of 91.51% has been obtained.

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“…An OTSu threshold segmentation technique has been applied to rOI of joint space area of the knee to get the clear joint gap but its computation time is high when the number of threshold increases. Table .4 gives the comparative study of various algorithms have been used for segmentation process (Chan et al, 2020;Palacios and Laura, 2021;Chai et al, 2021). Classification: The research on Knee OA can be carried out in two ways.…”

Section: Machine Learning In Knee Osteoarthritismentioning

confidence: 99%

Potential studies on Knee Osteoarthritis Segmentation and Classification

Sivakumari¹

2021

Biosc Biotech Res Comm

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Osteoarthritis detection by applying quadtree analysis to human joint knee X-ray imagery

Cited by 7 publications

References 25 publications

Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted

Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted

Detection and Classification of The Osteoarthritis in Knee Joint Using Transfer Learning with Convolutional Neural Networks (CNNs)

Potential studies on Knee Osteoarthritis Segmentation and Classification

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