Comparative analysis of machine learning and deep learning algorithms for knee arthritis detection using YOLOv8 models

Knee arthritis is a prevalent joint condition that affects many people worldwide. Early detection and appropriate treatment are essential to slow the disease's progression and enhance patients' quality of life. In this study, various machine learning and deep learning algorithms were used...

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Vydané v:Journal of X-ray science and technology Ročník 33; číslo 3; s. 565
Hlavný autor: Cinar, Ilkay
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States 01.05.2025
Predmet:
Algorithms
Deep Learning
Humans
Image Interpretation, Computer-Assisted - methods
Knee Joint - diagnostic imaging
Machine Learning
Neural Networks, Computer
Osteoarthritis, Knee - diagnostic imaging
deep learning
knee arthritis detection
classification
machine learning
YOLOv8
ISSN:1095-9114, 1095-9114
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Shrnutí:Knee arthritis is a prevalent joint condition that affects many people worldwide. Early detection and appropriate treatment are essential to slow the disease's progression and enhance patients' quality of life. In this study, various machine learning and deep learning algorithms were used to detect knee arthritis. The machine learning models included k-NN, SVM, and GBM, while DenseNet, EfficientNet, and InceptionV3 were used as deep learning models. Additionally, YOLOv8 classification models (YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, and YOLOv8x-cls) were employed. The "Annotated Dataset for Knee Arthritis Detection" with five classes (Normal, Doubtful, Mild, Moderate, Severe) and 1650 images were divided into 80% training, 10% validation, and 10% testing using the Hold-Out method. YOLOv8 models outperformed both machine learning and deep learning algorithms. k-NN, SVM, and GBM achieved success rates of 63.61%, 64.14%, and 67.36%, respectively. Among deep learning models, DenseNet, EfficientNet, and InceptionV3 achieved 62.35%, 70.59%, and 79.41%. The highest success was seen in the YOLOv8x-cls model at 86.96%, followed by YOLOv8l-cls at 86.79%, YOLOv8m-cls at 83.65%, YOLOv8s-cls at 80.37%, and YOLOv8n-cls at 77.91%.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1095-9114
1095-9114
DOI:10.1177/08953996241308770