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An ANN model for the differential diagnosis of tuberculosis and sarcoidosis


Mahalakshmi Vijayaraj¶, PA Abhinand&, P Venkatesan&, PK Ragunath*



1Department of Bioinformatics, Faculty of Biomedical Sciences Sri Ramachandra Institute of Higher Education and Research (Deemed To Be University),



E-mail: hod.bioinformatics@sriramachandra.edu.in; *Corresponding author, ¶,&equal contribution

Article Type

Research Article



Submitted on June 3, 2020; Revision June 8, 2020; Accepted June 8, 2020; Published July 31, 2020



Sarcoidosis is often misdiagnosed as tuberculosis and consequently mistreated owing to inherent limitations in histopathological and radiological presentations. It is known that the differential diagnosis of Tuberculosis and Sarcoidosis is often non-trivial and requires expertise and experience from clinicians. Therefore, it is of interest to describe a multilayer neural network model to differentiate pulmonary tuberculosis from Sarcoidosis using signal intensity data from blood transcriptional microarray. Genes that are significantly upregulated in Pulmonary Tuberculosis & Sarcoidosis in comparison with healthy controls were used in the model. The model classified Pulmonary Tuberculosis & Sarcoidosis with 95.8% accuracy. The model also helps to identify gene markers that are differentially upregulated in the two clinical conditions.



Machine learning, Artificial Neural Network (ANN), Multi-layer perceptron (MLP), Pulmonary Tuberculosis (PTB), Sarcoidosis.



Vijayaraj et al. Bioinformation 16(7): 539-546 (2020) 


Edited by

P Kangueane






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.