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Rethinking densely connected convolutional networks for diagnosing infectious diseases.

Podder, Prajoy; Alam, Fatema Binte; Mondal, M. Rubaiyat Hossain; Hasan, Md Junayed; Rohan, Ali; Bharati, Subrato


Prajoy Podder

Fatema Binte Alam

M. Rubaiyat Hossain Mondal

Subrato Bharati


Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model's performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The mode'ls ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.


PODDER, P., ALAM, F.B., MONDAL, M.R.H., HASAN, M.J., ROHAN, A. and BHARATI, S. 2023. Rethinking densely connected convolutional networks for diagnosing infectious diseases. Computers [online], 12(5), article 95. Available from:

Journal Article Type Article
Acceptance Date Apr 28, 2023
Online Publication Date May 2, 2023
Publication Date May 1, 2023
Deposit Date May 7, 2023
Publicly Available Date Jun 8, 2023
Journal Computers
Electronic ISSN 2073-431X
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 12
Issue 5
Article Number 95
Keywords Convolutional neural network; Deep learning; COVID-19; DenseNet 169; DenseNet 201; Transfer learning
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