ANALYSIS OF EXISTENCE OF RHEUMATOID ARTHRITIS IN GIVEN SUBJECT WITH ASSISTANCE TO INTERPRET RA IMPACT FACTOR DATA

ANALYSIS OF EXISTENCE OF RHEUMATOID ARTHRITIS IN GIVEN SUBJECT WITH ASSISTANCE TO INTERPRET RA IMPACT FACTOR DATA.

1Divya B, 2Balasurya M, 3Rithik J, 4Selvakumar M

1UG Scholar, Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, India, divya.cs20@bitsathy.ac.in

2UG Scholar, Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, India, balasurya.it20@bitsathy.ac.in,

3UG Scholar, Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Erode, India, rithik.cs20@bitsathy.ac.in

4Assistant Professor, Department of Information Technology, Bannari Amman Institute of Technology, Erode, India, selvakumarm@bitsathy.ac.in.

ABSTRACT

This study proposes an original methodology for distinguishing Rheumatoid Joint pain (RA) utilizing Convolutional Brain Organizations (CNNs) in profound learning. The strategy uses a dataset of clinical pictures, preprocesses them, and feeds them into a CNN model. The CNN design catches pertinent highlights and utilizes completely associated layers for grouping. Execution assessment incorporates precision, awareness, explicitness, and AUC-ROC. Starter results show promising execution, demonstrating the capability of the methodology for early RA discovery. The proposed technique holds guarantee for helping clinical experts in convenient mediation and customized treatment plans. Future work includes approval on bigger datasets and investigating multi-modular information joining for further developed precision and visor.

Keywords: Rheumatoid Arthritis, CNN (Convolution Neural Network), Deep Learning