Research Area:  Machine Learning

Abstract:

The medical diagnosis method is complex and consisting of a lot of vagueness due to imprecision and subjectivity, Magnetic resonance images have a great deal of resonance with this complexity due to its insightful nature. As rheumatoid arthritis is a chronic inflammatory condition, the modality of magnetic resonance imaging plays an important role in delivering in-depth research. Through gathering vast quantities of magnetic resonance data patterns, the early onset of rheumatoid arthritis can be studied. Artificial intelligence approaches have been used to help physicians in addressing these challenges and to make knowledgeable and accurate choices in the diagnosis of diseases. A variety of papers and diverse methods have been written to resolve concerns and problems. Inclusion and exclusion criteria are classified and evaluated to demonstrate the effect of artificial intelligence to enhance the diagnosis of the disease. The paper further explores the common classification methods for the diagnosis of rheumatoid arthritis. This analysis aims to explain new advances to increase the rate of identification and diagnosis of rheumatoid arthritis.

Keywords:  
rheumatoid arthritis
medical diagnosis
vagueness
imprecision
subjectivity
magnetic resonance
artificial intelligence

Author(s) Name:  Sujeet More, Jimmy Singla

Publisher name:  IEEE

DOI:  https://doi.org/10.1109/ICCES51350.2021.9489144

Volume Information:  -

Paper Link: https://ieeexplore.ieee.org/abstract/document/9489144