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Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases.
Comput Biol Med. 2021 05; 132:104348.CB

Abstract

Corona Virus Disease (COVID-19) has been announced as a pandemic and is spreading rapidly throughout the world. Early detection of COVID-19 may protect many infected people. Unfortunately, COVID-19 can be mistakenly diagnosed as pneumonia or lung cancer, which with fast spread in the chest cells, can lead to patient death. The most commonly used diagnosis methods for these three diseases are chest X-ray and computed tomography (CT) images. In this paper, a multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer from a combination of chest x-ray and CT images is proposed. This combination has been used because chest X-ray is less powerful in the early stages of the disease, while a CT scan of the chest is useful even before symptoms appear, and CT can precisely detect the abnormal features that are identified in images. In addition, using these two types of images will increase the dataset size, which will increase the classification accuracy. To the best of our knowledge, no other deep learning model choosing between these diseases is found in the literature. In the present work, the performance of four architectures are considered, namely: VGG19-CNN, ResNet152V2, ResNet152V2 + Gated Recurrent Unit (GRU), and ResNet152V2 + Bidirectional GRU (Bi-GRU). A comprehensive evaluation of different deep learning architectures is provided using public digital chest x-ray and CT datasets with four classes (i.e., Normal, COVID-19, Pneumonia, and Lung cancer). From the results of the experiments, it was found that the VGG19 +CNN model outperforms the three other proposed models. The VGG19+CNN model achieved 98.05% accuracy (ACC), 98.05% recall, 98.43% precision, 99.5% specificity (SPC), 99.3% negative predictive value (NPV), 98.24% F1 score, 97.7% Matthew's correlation coefficient (MCC), and 99.66% area under the curve (AUC) based on X-ray and CT images.

Authors+Show Affiliations

Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta, 31733, Egypt; Department of Information Technology, College of Computer, Qassim University, Buraydah, 51452, Saudi Arabia. Electronic address: d.hussein@qu.edu.sa.Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta, 31733, Egypt. Electronic address: Nada_elshennawy@f-eng.tanta.edu.eg.Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta, 31733, Egypt. Electronic address: amany_sarhan@f-eng.tanta.edu.eg.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

33774272

Citation

Ibrahim, Dina M., et al. "Deep-chest: Multi-classification Deep Learning Model for Diagnosing COVID-19, Pneumonia, and Lung Cancer Chest Diseases." Computers in Biology and Medicine, vol. 132, 2021, p. 104348.
Ibrahim DM, Elshennawy NM, Sarhan AM. Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Comput Biol Med. 2021;132:104348.
Ibrahim, D. M., Elshennawy, N. M., & Sarhan, A. M. (2021). Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Computers in Biology and Medicine, 132, 104348. https://doi.org/10.1016/j.compbiomed.2021.104348
Ibrahim DM, Elshennawy NM, Sarhan AM. Deep-chest: Multi-classification Deep Learning Model for Diagnosing COVID-19, Pneumonia, and Lung Cancer Chest Diseases. Comput Biol Med. 2021;132:104348. PubMed PMID: 33774272.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. AU - Ibrahim,Dina M, AU - Elshennawy,Nada M, AU - Sarhan,Amany M, Y1 - 2021/03/19/ PY - 2021/02/01/received PY - 2021/03/13/revised PY - 2021/03/13/accepted PY - 2021/3/29/pubmed PY - 2021/6/22/medline PY - 2021/3/28/entrez KW - COVID-19 detection KW - CT images KW - Chest X-ray KW - Deep learning KW - Lung cancer KW - Pneumonia SP - 104348 EP - 104348 JF - Computers in biology and medicine JO - Comput Biol Med VL - 132 N2 - Corona Virus Disease (COVID-19) has been announced as a pandemic and is spreading rapidly throughout the world. Early detection of COVID-19 may protect many infected people. Unfortunately, COVID-19 can be mistakenly diagnosed as pneumonia or lung cancer, which with fast spread in the chest cells, can lead to patient death. The most commonly used diagnosis methods for these three diseases are chest X-ray and computed tomography (CT) images. In this paper, a multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer from a combination of chest x-ray and CT images is proposed. This combination has been used because chest X-ray is less powerful in the early stages of the disease, while a CT scan of the chest is useful even before symptoms appear, and CT can precisely detect the abnormal features that are identified in images. In addition, using these two types of images will increase the dataset size, which will increase the classification accuracy. To the best of our knowledge, no other deep learning model choosing between these diseases is found in the literature. In the present work, the performance of four architectures are considered, namely: VGG19-CNN, ResNet152V2, ResNet152V2 + Gated Recurrent Unit (GRU), and ResNet152V2 + Bidirectional GRU (Bi-GRU). A comprehensive evaluation of different deep learning architectures is provided using public digital chest x-ray and CT datasets with four classes (i.e., Normal, COVID-19, Pneumonia, and Lung cancer). From the results of the experiments, it was found that the VGG19 +CNN model outperforms the three other proposed models. The VGG19+CNN model achieved 98.05% accuracy (ACC), 98.05% recall, 98.43% precision, 99.5% specificity (SPC), 99.3% negative predictive value (NPV), 98.24% F1 score, 97.7% Matthew's correlation coefficient (MCC), and 99.66% area under the curve (AUC) based on X-ray and CT images. SN - 1879-0534 UR - https://www.unboundmedicine.com/medline/citation/33774272/Deep_chest:_Multi_classification_deep_learning_model_for_diagnosing_COVID_19_pneumonia_and_lung_cancer_chest_diseases_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0010-4825(21)00142-6 DB - PRIME DP - Unbound Medicine ER -