• Kadirimangalam Giravani
• Dr. B Anuradha

Rheumatoid Arthritis (RA) is a prevalent disease characterized by joint inflammation, discomfort, tenderness, and potential involvement of other body organs in severe cases. Elevated vascular disorder is often observed in the vicinity of inflamed tissue, and autoimmune lesions in joints are associated with heightened fever. Traditional RA detection primarily relies on blood sample tests. However, this work introduces an innovative approach for RA detection utilizing Convolutional Neural Network (CNN) classification applied to X-ray images. This work need clear X-ray images as input, and following preprocessing and segmentation and Convolutional Neural Network (CNN) classifier for accurate discrimination between abnormal and normal images. Various evaluation metrics are employed to gauge the performance of the model. Through the utilization of an optimized Convolutional Neural Network (CNN) architecture, substantial enhancements in the accuracy of image classification are achieved. The proposed Convolutional Neural Network (CNN)-based model exhibits high effectiveness in precisely distinguishing between RA-positive and RA-negative samples.

Rheumatoid Arthritis (RA), Convolutional Neural Networks (CNN), etc.

"Rheumatoid Arthritis Detection using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.f403-f415, April-2024, Available :http://www.jetir.org/papers/JETIR2404543.pdf

"Rheumatoid Arthritis Detection using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppf403-f415, April-2024, Available at : http://www.jetir.org/papers/JETIR2404543.pdf