Improving lung disease detection by joint learning with COVID-19 radiography database

Hery Harjono Muljo, Bens Pardamean, Kartika Purwandari, Tjeng Wawan Cenggoro

Abstract


Diagnostic chest radiography is one of the most common imaging tests performed in medical practice. A radiology workflow goal is to detect, diagnose, and manage diseases using chest radiography in an automated, timely, and accurate manner. Radiography data have proved very effective for assessing COVID-19 patients, particularly for treating overcrowded emergency departments and hospitals. The use of Deep Learning (DL) methods in Artificial Intelligence (AI) has become dominant in detecting diseases via chest X-rays. This study utilized the COVID-19 Radiographic Database and the National Institutes of Health (NIH) Chest-Xray to study pre-training fine-tuning of the DL model on chest radiographic images. We investigate the robust network architecture in detail: DenseNet-121, in this dataset dual technique to improve insight into the different methods and their application to chest X-ray classification. Consequently, this dual dataset technique is able to provide better detection results for each cluster of lung diseases. AUC results obtained using DenseNet-121 reached an average of 82.16 percent, with the highest AUC reaching 99.99% in the cluster containing Viral Pneumonia lung disease.

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Published: 2022-01-03

How to Cite this Article:

Hery Harjono Muljo, Bens Pardamean, Kartika Purwandari, Tjeng Wawan Cenggoro, Improving lung disease detection by joint learning with COVID-19 radiography database, Commun. Math. Biol. Neurosci., 2022 (2022), Article ID 1

Copyright © 2022 Hery Harjono Muljo, Bens Pardamean, Kartika Purwandari, Tjeng Wawan Cenggoro. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

ISSN 2052-2541

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