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

Abstract

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.

Citation

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: https://doi.org/10.3390/computers12050095

• 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
• DOI: https://doi.org/10.3390/computers12050095
• Keywords: Convolutional neural network; Deep learning; COVID-19; DenseNet 169; DenseNet 201; Transfer learning
• Public URL: https://rgu-repository.worktribe.com/output/1953142