Knee Osteoarthritis Detection and It’s Severity CNN

Puthuru Kavya; Kouluri Sreeja Reddy; Tholla Ujwala; Amuru Mounitha; Ms. Rekha M S

doi:10.32628/CSEIT2410322

Authors

Puthuru Kavya Computer Science and Engineering, R. L. Jalappa Institute of Technology, Doddaballapura, Bengaluru, India Author
Kouluri Sreeja Reddy Computer Science and Engineering, R. L. Jalappa Institute of Technology, Doddaballapura, Bengaluru, India Author
Tholla Ujwala Computer Science and Engineering, R. L. Jalappa Institute of Technology, Doddaballapura, Bengaluru, India Author
Amuru Mounitha Computer Science and Engineering, R. L. Jalappa Institute of Technology, Doddaballapura, Bengaluru, India Author
Ms. Rekha M S Assistant Professor, Computer Science and Engineering, R. L. Jalappa Institute of Technology, Doddaballapura, Bengaluru, India Author

DOI:

https://doi.org/10.32628/CSEIT2410322

Keywords:

Convolutional Neural Networks, Osteoarthritis, X-rays

Abstract

Osteoarthritis (OA) of the knee is a common degenerative joint disease that is characterized by inflammation and cartilage degradation, which results in pain and impairment. For prompt intervention and care, early detection and precise assessment of the severity of OA are essential. In this research, we offer a unique method for automated knee OA diagnosis and severity assessment using medical imaging data, especially X-ray images, using convolutional neural networks (CNNs). Our CNN architecture is intended to identify complex features from knee X-rays and categorize them into various OA severity levels, from moderate to severe. A sizable dataset of knee X-ray pictures with accompanying OA severity scores is used by the suggested model. Our method shows promise in correctly detecting knee OA after thorough testing and validation on a variety of datasets.

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