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LDDNet: a deep learning framework for the diagnosis of infectious lung diseases.

Podder, Prajoy; Das, Sanchita Rani; Mondal, M. Rubaiyat Hossain; Bharati, Subrato; Maliha, Azra; Hasan, Md Junayed; Piltan, Farzin

Authors

Prajoy Podder

Sanchita Rani Das

M. Rubaiyat Hossain Mondal

Subrato Bharati

Azra Maliha

Farzin Piltan



Abstract

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.

Citation

PODDER, P., RANI DAS, S., MONDAL, M.R.H., BHARATI, S., MALIHA, A., HASAN, M.J. and PILTAN, F. 2023. LDDNet: a deep learning framework for the diagnosis of infectious lung diseases. Sensors [online], 23(1), article 480. Available from: https://doi.org/10.3390/s23010480

Journal Article Type Article
Acceptance Date Dec 27, 2022
Online Publication Date Jan 2, 2023
Publication Date Jan 1, 2023
Deposit Date Jan 4, 2023
Publicly Available Date Jan 19, 2023
Journal Sensors
Print ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 23
Issue 1
Article Number 480
DOI https://doi.org/10.3390/s23010480
Keywords Infectious disease; COVID-19; CT scan; X-ray; ResNet152V2; DenseNet201; XceptionNet
Public URL https://rgu-repository.worktribe.com/output/1848588

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