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Early Arthritis Detection Using Convolutional Neural Networks for Enhanced Diagnostic Accuracy.

Kamini Solanki1, Arpitkumar Shah2, Arpankumar Raval1

  • 1Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, Charotar University of Science and Technology, CHARUSAT, Campus, Changa, 388421, Anand, Gujarat, India.

Current Neurovascular Research
|July 6, 2026
PubMed
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This study developed a deep learning model using Convolutional Neural Networks (CNNs) for automated arthritis detection in knee X-rays. The system achieved 95% accuracy, offering a reliable and efficient tool for early diagnosis and clinical support.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Orthopedics

Background:

  • Arthritis significantly impacts global quality of life, necessitating early detection for effective intervention.
  • Traditional radiographic diagnosis of arthritis relies on expert interpretation, which can introduce variability and delays.
  • Automated, precise, and timely identification of arthritis is crucial for improved patient outcomes.

Purpose of the Study:

  • To develop an automated deep learning mechanism for precise and timely arthritis identification using knee X-ray images.
  • To overcome the limitations of subjective expert interpretation in conventional arthritis diagnosis.
  • To enhance early clinical intervention and patient care through accurate diagnostic support.

Main Methods:

  • A Convolutional Neural Network (CNN) framework was designed for automated arthritis detection from knee X-ray images.
Keywords:
Arthritis detectionconvolutional neural networkdeep learningmedical image analysisosteoarthritis

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Last Updated: Jul 7, 2026

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  • The model was trained and validated on a Kaggle dataset comprising 4414 knee radiographs.
  • Performance was evaluated using accuracy, precision, recall, confusion matrix, and ROC analysis, with Grad-CAM for interpretability.

    • Main Results:

    • The proposed CNN model achieved an overall classification accuracy of 95%.
    • The system demonstrated high precision (0.98) and recall (0.93) in differentiating arthritic from normal knee conditions.
    • Grad-CAM visualizations successfully highlighted key radiographic features used by the model for prediction, enhancing interpretability.

    Conclusions:

    • Deep learning analysis of knee radiographs provides a reliable and efficient method for arthritis detection.
    • Interpretability tools like Grad-CAM increase trust and transparency, making the model more applicable to clinical settings.
    • The CNN-based system supports clinicians in musculoskeletal imaging, aiding early screening and patient management.