Machine Learning Approaches for the Classification of Knee Osteoarthritis

Tariq, Tayyaba; Suhail, Zobia; Nawaz, Zubair

doi:10.1109/iceccme57830.2023.10252236

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…This evolution in medical diagnostics promises improved patient care through timely interventions, highlighting the significant impact of ML and DL in enhancing diagnostic processes specifically. In the context of KOA detection [16][17][18][19][20][21][22][23][24][25][26][27][28], radiographic images have been successfully analyzed using a spectrum of ML and DL algorithms with promising results. For instance, L. Anifah's team tackled knee grading through image processing and self-organizing maps, observing varied accuracy across KL grades [16].…”

Section: Introductionmentioning

confidence: 99%

“…R. Mahum et al (2021) introduced a DL strategy for early KOA detection, employing hybrid feature models and multi-class classifiers to reach roughly 97% accuracy [24]. Similarly, T. Tariq (2023) developed a method combining pre-trained CNNs and transfer learning for KOA classification, achieving up to 90.8% accuracy in binary models [27]. These studies highlight the versatility and effectiveness of ML and DL in improving KOA diagnostic processes.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Machine Learning Classifiers for Enhancing Knee Osteoarthritis Diagnosis

Raza,

Phan,

et al. 2024

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Knee osteoarthritis (KOA) is a leading cause of disability, particularly affecting older adults due to the deterioration of articular cartilage within the knee joint. This condition is characterized by pain, stiffness, and impaired movement, posing a significant challenge in medical diagnostics and treatment planning, especially due to the current inability for early and accurate detection or monitoring of disease progression. This research introduces a multifaceted approach employing feature extraction and machine learning (ML) to improve the accuracy of diagnosing and classifying KOA stages from radiographic images. Utilizing a dataset of 3154 knee X-ray images, this study implemented feature extraction methods such as Histogram of Oriented Gradients (HOG) with Linear Discriminant Analysis (LDA) and Min–Max scaling to prepare the data for classification. The study evaluates six ML classifiers—K Nearest Neighbors classifier, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree, Random Forest, and XGBoost—optimized via GridSearchCV for hyperparameter tuning within a 10-fold Stratified K-Fold cross-validation framework. An ensemble model has also been made for the already high-accuracy models to explore the possibility of enhancing the accuracy and reducing the risk of overfitting. The XGBoost classifier and the ensemble model emerged as the most efficient for multiclass classification, with an accuracy of 98.90%, distinguishing between healthy and unhealthy knees. These results underscore the potential of integrating advanced ML methodologies for the nuanced and accurate diagnosis and classification of KOA, offering new avenues for clinical application and future research in medical imaging diagnostics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Machine Learning Classifiers for Enhancing Knee Osteoarthritis Diagnosis

Raza,

Phan,

et al. 2024

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“…According to Kim et al (2020), subjectivity causes fluctuations in both intra-and inter-observer dependability. As a result, numerous research (Zhang et al, 2020;Tariq et al, 2023;Teoh et al, 2022;Norman et al, 2019) find limited accuracy in early-stage KOA identification. While some methods include challenging stages for classification, others combine clinical data and X-ray features to improve performance (Leung et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Ensemble CNN Model for Computer-Aided Knee Osteoarthritis Diagnosis

Bhateja,

Dubey,

Maurya

et al. 2024

International Journal of Service Science, Management, Engineering, and Technology

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Osteoarthritis (OA) is a multifaceted ailment posing challenges in its diagnosis and treatment due to the intricate nature of the disease. Particularly, Knee Osteoarthritis (KOA) significantly impacts the knee joint, manifesting through symptoms such as pain, stiffness, and limited movement. Despite its prevalence and debilitating effects, early detection of KOA remains elusive, often hindered by subjective diagnostic methods and the absence of reliable biomarkers. This research aims to address these challenges by leveraging deep learning techniques and ensemble methodologies for accurate KOA classification using knee X-ray images. This paper utilized a dataset sourced from the Osteoarthritis Initiative (OAI), comprising a large collection of knee X-ray images graded according to the Kellgren-Lawrence (KL) grading system. The proposed design methodology involves preprocessing the input X-ray images and training multiple pre-trained Convolutional Neural Network (CNN) models, including ResNet50, InceptionResNetV2, and Xception to classify KOA severity grades. Additionally, this work introduced an ensemble model by combining predictions from these base models to improve overall performance of the Computer-Aided Diagnosis (CAD) system. The obtained results demonstrate the effectiveness of the ensemble approach, outperforming individual algorithms in terms of accuracy, precision, recall, F1-score, and balanced accuracy. However, challenges persist in accurately distinguishing between adjacent KL grades, particularly grades#1 and #2, highlighting the need for further refinement. Notably, the proposed CAD model showcases superior predictive accuracy compared to various state-of-art methods, offering a promising avenue for early KOA diagnosis and personalized treatment strategies.

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