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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Structural Classification of Joints01:20

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Comparative analysis of machine learning and deep learning algorithms for knee arthritis detection using YOLOv8

Ilkay Cinar1

  • 1Department of Computer Engineering, Selcuk University, 42250 Selcuklu, Konya, Türkiye.

Journal of X-Ray Science and Technology
|February 27, 2025
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Summary

This study shows YOLOv8 models are superior for detecting knee arthritis compared to traditional machine learning and deep learning methods. YOLOv8x-cls achieved the highest accuracy, offering a promising tool for early knee arthritis diagnosis.

Keywords:
YOLOv8classificationdeep learningknee arthritis detectionmachine learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Knee arthritis is a widespread condition impacting global health.
  • Early detection and treatment are crucial for managing knee arthritis progression and improving patient outcomes.
  • Advanced computational methods are being explored for more accurate diagnostic tools.

Purpose of the Study:

  • To evaluate and compare the performance of various machine learning (ML), deep learning (DL), and YOLOv8 classification models in detecting knee arthritis.
  • To identify the most effective algorithm for accurate knee arthritis classification from medical images.
  • To assess the utility of YOLOv8 models in a clinical context for knee arthritis diagnosis.

Main Methods:

  • Utilized the "Annotated Dataset for Knee Arthritis Detection" comprising 1650 images across five classes (Normal, Doubtful, Mild, Moderate, Severe).
  • Employed traditional ML models (k-NN, SVM, GBM), DL models (DenseNet, EfficientNet, InceptionV3), and YOLOv8 classification models (YOLOv8n-cls to YOLOv8x-cls).
  • Data was split using the Hold-Out method (80% training, 10% validation, 10% testing).

Main Results:

  • YOLOv8 models significantly outperformed ML and DL algorithms in knee arthritis detection.
  • ML models achieved accuracies ranging from 63.61% (k-NN) to 67.36% (GBM).
  • DL models showed varying success, with InceptionV3 reaching 79.41%, while YOLOv8x-cls achieved the highest accuracy at 86.96%.

Conclusions:

  • YOLOv8 classification models, particularly YOLOv8x-cls, demonstrate superior performance for knee arthritis detection.
  • These findings suggest YOLOv8 models offer a promising, highly accurate approach for automated knee arthritis diagnosis.
  • The study highlights the potential of advanced AI in enhancing early detection and management of knee arthritis.