Detection of Rheumatoid Arthritis Using CNN by Transfer Learning

Alam, Afroj; Ahamad, Muhammad Kalamuddin; Mohammed Aarif, K. O.; Anwar, Taushif

doi:10.1007/978-981-99-9029-0_5

Cited by 1 publication

(1 citation statement)

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“…Machine learning algorithms are potential tools that could be integrated into the diagnostic process for the evaluation and detection of rheumatic diseases [3,[11][12][13][14][15][16][17][18][19][20][21][22]. These algorithms have become important components in the diagnosis of conditions due to their inherent ability to analyze large datasets to identify trends and patterns that are not easily visible in the datasets and use the information to make predictions with high precision.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Tool for Early Detection of Rheumatic Disorders Using Machine Learning Algorithm and Predictive Models

Mills,

Dey,

Kassim

et al. 2024

BioMedInformatics

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Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clinical examinations, patient history, and laboratory investigations. Machine learning techniques have made it possible to integrate such techniques into the complex diagnostic process to identify inherent features that lead to disease formation, development, and progression for remedial measures. Methods: An automated diagnostic tool using a multilayer neural network computational engine is presented to detect rheumatic disorders and the type of underlying disorder for therapeutic strategies. Rheumatic disorders considered are rheumatoid arthritis, osteoarthritis, and systemic lupus erythematosus. The detection system was trained and tested using 70% and 30% respectively of labelled synthetic dataset of 100,000 records containing both single and multiple disorders. Results: The detection system was able to detect and predict underlying disorders with accuracy of 97.48%, sensitivity of 96.80%, and specificity of 97.50%. Conclusion: The good performance suggests that this solution is robust enough and can be implemented for screening patients for intervention measures. This is a much-needed solution in environments with limited specialists, as the solution promotes task-shifting from the specialist level to the primary healthcare physicians.

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