2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 1 , PP: 235-244, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Govindan Rajesh 1 * , Nandagopal Malarvizhi 2
Doi: https://doi.org/10.54216/FPA.210117
Arthritis significantly affects mobility and quality of life due to joint inflammation and dysfunction. Its most common type, rheumatoid arthritis (RA), primarily influences multiple joints and tissues, especially in women aged 30–50. Common symptoms include pain, swelling, and stiffness. The growing prevalence of RA, projected to reach 44 million globally by 2045, underscores the need for advanced diagnostic methods. MRI offers detailed visualization of joint structures, essential for accurate diagnosis. However, current grading systems like OARSI and Kellgren-Lawrence are subjective and prone to variability. This study introduces the KL Grading DeepNetX framework, a deep learning-based model for automated RA grading and classification. The approach integrates image preprocessing and segmentation to extract key features such as joint space narrowing and cartilage thickness. Comparative analysis shows that KL Grading DeepNetX outperforms traditional methods with high precision, sensitivity, specificity, and F1-score. This framework enables earlier, more accurate and efficient detection of arthritis using knee MRI images.
Deep Learning , KL Grading DeepNetX , Joint Space Narrowing , Magnetic Resonance Imaging , Rheumatoid Arthritis
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