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Open Access

Retraction

Retraction: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

  • The PLOS One Editors

The PLOS One Editors retract this article [1] due to concerns about compromised peer review.

In addition, in Fig 2 of this article [1], the top two rows in the columns labeled Covid-19, pneumonia, tuberculosis, lung cancer, edema, and consolidation lung appear similar to the COVID-19 Omicron and pneumonia rows in Fig 2 of [2, retracted in 3] and the Dataset 1 and Dataset 2 rows in Figure 1 of [4], despite representing different conditions.

TA did not agree with the retraction. HM either did not respond directly or could not be reached.

References

  1. 1. Malik H, Anees T. RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds. PLoS One. 2024;19(3):e0296352. pmid:38470893
  2. 2. Khan AH, Li J, Asghar MN, Iqbal S. RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans. PLoS One. 2025;20(8):e0327419. pmid:40779565
  3. 3. The PLOS One Editors. Retraction: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans. PLoS One. 2026;21(4):e0346202. pmid:41950175
  4. 4. Malik H, Anees T, Naeem A, Naqvi RA, Loh W-K. Blockchain-federated and deep-learning-based ensembling of capsule network with incremental extreme learning machines for classification of COVID-19 using CT scans. Bioengineering (Basel). 2023;10(2):203. pmid:36829697

Citation: The PLOS One Editors (2026) Retraction: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds. PLoS One 21(6): e0351197. https://doi.org/10.1371/journal.pone.0351197

Published: June 23, 2026

Copyright: © 2026 The PLOS One Editors. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.