Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID‐19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID‐19 dia...

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Published in:	Microscopy research and technique Vol. 85; no. 1; pp. 385 - 397
Main Authors:	Amin, Javeria, Anjum, Muhammad Almas, Sharif, Muhammad, Rehman, Amjad, Saba, Tanzila, Zahra, Rida
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 01.01.2022 Wiley Subscription Services, Inc
Subjects:	Artificial neural networks Classification Coders Computed tomography COVID-19 COVID-19 Testing Deep learning Deeplabv3 denoise convolutional neural network (DnCNN) healthcare Humans Image enhancement Image segmentation Machine learning Medical imaging Neural networks Neural Networks, Computer public health ResNet‐18 SARS-CoV-2 Sputum stack sparse autoencoder deep learning model (SSAE) Tomography, X-Ray Computed Deeplabv3 ResNet-18 denoise convolutional neural network (DnCNN) public health stack sparse autoencoder deep learning model (SSAE) healthcare
ISSN:	1059-910X, 1097-0029, 1097-0029
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Abstract	The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID‐19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID‐19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three‐phase model is proposed for COVID‐19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet‐18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto‐encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification. Denoise convolutional neural network regression model used for noise removal to enhance images quality. Model deeplabv3 is used as a backbone of the ResNet‐18 model to segment infected lungs region. Segmented images are further supplied to stack sparse autoencoder model for COVID‐19 classification.
AbstractList	The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID‐19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID‐19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three‐phase model is proposed for COVID‐19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet‐18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto‐encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification. Denoise convolutional neural network regression model used for noise removal to enhance images quality. Model deeplabv3 is used as a backbone of the ResNet‐18 model to segment infected lungs region. Segmented images are further supplied to stack sparse autoencoder model for COVID‐19 classification. The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID‐19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID‐19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three‐phase model is proposed for COVID‐19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet‐18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto‐encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification. The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification. The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID‐19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID‐19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three‐phase model is proposed for COVID‐19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet‐18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto‐encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification. Denoise convolutional neural network regression model used for noise removal to enhance images quality. Model deeplabv3 is used as a backbone of the ResNet‐18 model to segment infected lungs region. Segmented images are further supplied to stack sparse autoencoder model for COVID‐19 classification.
Author	Saba, Tanzila Amin, Javeria Zahra, Rida Anjum, Muhammad Almas Sharif, Muhammad Rehman, Amjad
AuthorAffiliation	2 Dean of University, National University of Technology (NUTECH) Islamabad Pakistan 4 Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh Saudi Arabia 3 Department of Computer Science COMSATS University Islamabad Wah Campus Wah Cantt Pakistan 1 Department of Computer Science University of Wah Wah Cantt Pakistan
AuthorAffiliation_xml	– name: 4 Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh Saudi Arabia – name: 1 Department of Computer Science University of Wah Wah Cantt Pakistan – name: 3 Department of Computer Science COMSATS University Islamabad Wah Campus Wah Cantt Pakistan – name: 2 Dean of University, National University of Technology (NUTECH) Islamabad Pakistan
Author_xml	– sequence: 1 givenname: Javeria surname: Amin fullname: Amin, Javeria organization: University of Wah – sequence: 2 givenname: Muhammad Almas surname: Anjum fullname: Anjum, Muhammad Almas organization: Dean of University, National University of Technology (NUTECH) – sequence: 3 givenname: Muhammad surname: Sharif fullname: Sharif, Muhammad organization: COMSATS University Islamabad Wah Campus – sequence: 4 givenname: Amjad orcidid: 0000-0002-3817-2655 surname: Rehman fullname: Rehman, Amjad email: rkamjad@gmail.com organization: CCIS Prince Sultan University – sequence: 5 givenname: Tanzila orcidid: 0000-0001-6718-3866 surname: Saba fullname: Saba, Tanzila organization: CCIS Prince Sultan University – sequence: 6 givenname: Rida surname: Zahra fullname: Zahra, Rida organization: University of Wah
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Cites_doi	10.1016/j.tmaid.2020.101623 10.1109/MITP.2020.3036820 10.1016/j.cmpb.2019.05.015 10.1148/radiol.2020200905 10.1016/j.ijsu.2020.04.018 10.1109/TIP.2017.2662206 10.1002/jemt.22998 10.1016/j.compeleceng.2020.106960 10.1007/978-3-030-01234-2_49 10.1109/CVPR.2016.90 10.1148/radiol.2019181960 10.1016/S2589-7500(20)30054-6 10.1016/j.compbiomed.2020.103795 10.1016/j.ijsu.2020.02.034 10.1002/jemt.22867 10.1002/jmv.25725 10.1186/s40779-020-00240-0 10.1109/ACCESS.2020.3016627 10.1016/j.eti.2021.101531 10.7150/ijbs.45134 10.1002/jemt.23702 10.1038/s41598-020-74164-z 10.1101/2020.07.16.20155093 10.1016/S0042-6989(97)00169-7 10.1109/ICCISci.2019.8716400 10.1109/ISBI48211.2021.9434047 10.1109/ICCISci.2019.8716449 10.1002/jemt.23429 10.1007/s10916-019-1475-2 10.1002/jemt.23275 10.1002/jemt.23326 10.1142/S0219519418500380 10.1109/ACCESS.2021.3094720 10.14569/IJARAI.2015.040406 10.1186/s41747-020-00173-2 10.1007/s12098-020-03263-6 10.1016/j.patcog.2021.107828 10.1007/s00521-019-04650-7 10.1148/radiol.2020200642
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References	2021; 9 2017; 80 2015; 4 2021; 22 2017; 26 2020; 121 2020; 83 2019; 79 2018; 81 2020; 16 2020; 78 2020; 34 2020; 32 2020; 10 2021a; 23 2020; 76 2021; 90 2021b; 23 2020; 7 2018; 18 2020; 4 2019; 82 2020; 2 2021; 113 2021 2020; 296 2020 1997; 37 2020; 92 2019 2018 2016 2020; 44 2020; 87 2019; 292 2021; 84 2019; 177 e_1_2_9_30_1 e_1_2_9_31_1 Amin J. (e_1_2_9_6_1) 2019; 79 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_12_1 e_1_2_9_33_1 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_14_1 e_1_2_9_39_1 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_41_1 e_1_2_9_42_1 e_1_2_9_20_1 e_1_2_9_40_1 e_1_2_9_22_1 e_1_2_9_45_1 e_1_2_9_21_1 e_1_2_9_46_1 e_1_2_9_24_1 e_1_2_9_43_1 e_1_2_9_23_1 e_1_2_9_44_1 e_1_2_9_8_1 e_1_2_9_7_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_2_1 e_1_2_9_9_1 e_1_2_9_26_1 e_1_2_9_25_1 e_1_2_9_28_1 e_1_2_9_47_1 e_1_2_9_27_1 e_1_2_9_29_1
References_xml	– volume: 292 start-page: 365 issue: 2 year: 2019 end-page: 373 article-title: Deep learning–based image conversion of CT reconstruction kernels improves Radiomics reproducibility for pulmonary nodules or masses publication-title: Radiology – volume: 4 start-page: 1 issue: 4 year: 2015 end-page: 8 article-title: Lung cancer detection on CT scan images: A review on the analysis techniques publication-title: International Journal of Advanced Research in Artificial Intelligence – volume: 26 start-page: 3142 issue: 7 year: 2017 end-page: 3155 article-title: Beyond a gaussian denoiser: Residual learning of deep CNN for Image Denoising publication-title: IEEE Transactions on Image Processing – volume: 84 start-page: 1462 year: 2021 end-page: 1474 article-title: Machine learning techniques to detect and forecast the daily total COVID‐19 infected and deaths cases under different lockdown types publication-title: Microscopy Research and Technique – volume: 90 year: 2021 article-title: Prediction of COVID‐19 ‐ pneumonia based on selected deep features and one class kernel extreme learning machine publication-title: Computers & Electrical Engineering – volume: 82 start-page: 1256 issue: 8 year: 2019 end-page: 1266 article-title: Lungs nodule detection framework from computed tomography images using support vector machine publication-title: Microscopy Research and Technique – volume: 44 start-page: 37 issue: 2 year: 2020 article-title: A deep learning approach for automated diagnosis and multi‐class classification of Alzheimer's disease stages using resting‐state fMRI and residual neural networks publication-title: Journal of Medical Systems – volume: 34 start-page: 101623 year: 2020 article-title: Clinical, laboratory and imaging features of COVID‐19: A systematic review and meta‐analysis publication-title: Travel Medicine and infectious Disease – volume: 22 year: 2021 article-title: Viral reverse engineering using artificial intelligence and big data COVID‐19 infection with long short‐term memory (LSTM) publication-title: Environmental Technology & Innovation – volume: 78 start-page: 185 year: 2020 end-page: 193 article-title: The socio‐economic implications of the coronavirus and COVID‐19 pandemic: a review publication-title: International Journal of Surgery – volume: 23 start-page: 63 year: 2021a end-page: 68 article-title: Deep learning‐based COVID‐19 detection using CT and X‐ray images: Current analytics and comparisons publication-title: IEEE IT Professional – volume: 296 start-page: E32 issue: 2 year: 2020 end-page: E40 article-title: Correlation of chest CT and RT‐PCR testing in coronavirus disease 2019 (COVID‐19) in China: A report of 1014 cases publication-title: Radiology – year: 2021 – volume: 296 issue: 2 year: 2020 article-title: Artificial intelligence distinguishes COVID‐19 from community‐acquired pneumonia on chest CT publication-title: Radiology – volume: 177 start-page: 69 year: 2019 end-page: 79 article-title: Brain tumor detection using statistical and machine learning method publication-title: Computer Methods and Programs in Biomedicine – year: 2016 – year: 2018 – volume: 121 year: 2020 article-title: Application of deep learning technique to manage COVID‐19 in routine clinical practice using CT images: Results of 10 convolutional neural networks publication-title: Computers in Biology and Medicine – volume: 92 start-page: 589 year: 2020 end-page: 594 article-title: Evaluation of coronavirus in tears and conjunctival secretions of patients with SARS‐CoV‐2 infection publication-title: Journal of medical virology – volume: 4 start-page: 50 issue: 1 year: 2020 article-title: Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem publication-title: European Radiology Experimental – volume: 80 start-page: 799 issue: 17 year: 2017 end-page: 811 article-title: Retinal imaging analysis based on vessel detection publication-title: Microscopy Research and Technique – volume: 32 start-page: 15965 year: 2020 end-page: 15973 article-title: Brain tumor detection: A long short‐term memory (LSTM)‐based learning model publication-title: Neural Computing and Applications – volume: 9 start-page: 35256 year: 2021 end-page: 35278 article-title: Hybrid segmentation method with confidence region detection for tumor identification publication-title: IEEE Access – year: 2020 – volume: 83 start-page: 410 issue: 4 year: 2020 end-page: 423 article-title: Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction publication-title: Microscopy Research and Technique – volume: 81 start-page: 449 issue: 5 year: 2018 end-page: 457 article-title: Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM publication-title: Microscopy Research and Technique – volume: 79 start-page: 10955 issue: 15 year: 2019 end-page: 10973 article-title: Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions publication-title: Multimedia Tools & Applications – volume: 113 start-page: 107828 year: 2021 article-title: Synergistic learning of lung lobe segmentation and hierarchical multi‐instance classification for automated severity assessment of COVID‐19 in CT images publication-title: Pattern Recognition – volume: 76 start-page: 71 year: 2020 end-page: 76 article-title: World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID‐19) publication-title: International Journal of Surgery – volume: 16 issue: 10 year: 2020 article-title: COVID‐19: What has been learned and to be learned about the novel coronavirus disease publication-title: International Journal of Biological Sciences – volume: 37 start-page: 3311 issue: 23 year: 1997 end-page: 3325 article-title: Sparse coding with an overcomplete basis set: A strategy employed by V1? publication-title: Vision Research – volume: 10 year: 2020 article-title: A comprehensive study on classification of COVID‐19 on computed tomography with pretrained convolutional neural networks publication-title: Scientific Reports – volume: 23 start-page: 63 year: 2021b end-page: 68 article-title: Real‐time diagnosis system of COVID‐19 using X‐ray images and deep learning publication-title: IEEE IT Professional – year: 2019 – volume: 18 issue: 04 year: 2018 article-title: Automated ulcer and bleeding classification from WCE images using multiple features fusion and selection publication-title: Journal of Mechanics in Medicine and Biology – volume: 87 start-page: 281 year: 2020 end-page: 286 article-title: A review of coronavirus disease‐2019 (COVID‐19) publication-title: Indian Journal of Pediatrics – volume: 7 start-page: 1 issue: 1 year: 2020 end-page: 10 article-title: The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID‐19) outbreak–an update on the status publication-title: Military Medical Research – volume: 82 issue: 9 year: 2019 article-title: Automated lung nodule detection and classification based on multiple classifiers voting publication-title: Microsc Res Tech. – volume: 2 start-page: e166 issue: 4 year: 2020 end-page: e167 article-title: COVID‐19 and artificial intelligence: Protecting health‐care workers and curbing the spread publication-title: Lancet Digital Health – ident: e_1_2_9_42_1 – ident: e_1_2_9_37_1 doi: 10.1016/j.tmaid.2020.101623 – ident: e_1_2_9_35_1 doi: 10.1109/MITP.2020.3036820 – ident: e_1_2_9_3_1 doi: 10.1016/j.cmpb.2019.05.015 – ident: e_1_2_9_26_1 doi: 10.1148/radiol.2020200905 – ident: e_1_2_9_30_1 doi: 10.1016/j.ijsu.2020.04.018 – ident: e_1_2_9_46_1 doi: 10.1109/TIP.2017.2662206 – ident: e_1_2_9_14_1 doi: 10.1002/jemt.22998 – ident: e_1_2_9_36_1 doi: 10.1109/MITP.2020.3036820 – ident: e_1_2_9_23_1 doi: 10.1016/j.compeleceng.2020.106960 – ident: e_1_2_9_9_1 doi: 10.1007/978-3-030-01234-2_49 – ident: e_1_2_9_17_1 doi: 10.1109/CVPR.2016.90 – ident: e_1_2_9_20_1 – ident: e_1_2_9_10_1 doi: 10.1148/radiol.2019181960 – ident: e_1_2_9_29_1 doi: 10.1016/S2589-7500(20)30054-6 – ident: e_1_2_9_7_1 doi: 10.1016/j.compbiomed.2020.103795 – ident: e_1_2_9_41_1 doi: 10.1016/j.ijsu.2020.02.034 – ident: e_1_2_9_19_1 doi: 10.1002/jemt.22867 – ident: e_1_2_9_43_1 doi: 10.1002/jmv.25725 – ident: e_1_2_9_47_1 – ident: e_1_2_9_15_1 doi: 10.1186/s40779-020-00240-0 – ident: e_1_2_9_12_1 doi: 10.1109/ACCESS.2020.3016627 – ident: e_1_2_9_16_1 doi: 10.1016/j.eti.2021.101531 – ident: e_1_2_9_45_1 doi: 10.7150/ijbs.45134 – ident: e_1_2_9_39_1 doi: 10.1002/jemt.23702 – ident: e_1_2_9_32_1 doi: 10.1038/s41598-020-74164-z – ident: e_1_2_9_8_1 doi: 10.1101/2020.07.16.20155093 – ident: e_1_2_9_31_1 doi: 10.1016/S0042-6989(97)00169-7 – ident: e_1_2_9_22_1 doi: 10.1109/ICCISci.2019.8716400 – ident: e_1_2_9_13_1 doi: 10.1109/ISBI48211.2021.9434047 – ident: e_1_2_9_4_1 doi: 10.1109/ICCISci.2019.8716449 – ident: e_1_2_9_34_1 doi: 10.1002/jemt.23429 – ident: e_1_2_9_44_1 – volume: 79 start-page: 10955 issue: 15 year: 2019 ident: e_1_2_9_6_1 article-title: Use of machine intelligence to conduct analysis of human brain data for detection of abnormalities in its cognitive functions publication-title: Multimedia Tools & Applications – ident: e_1_2_9_33_1 doi: 10.1007/s10916-019-1475-2 – ident: e_1_2_9_25_1 doi: 10.1002/jemt.23275 – ident: e_1_2_9_38_1 doi: 10.1002/jemt.23326 – ident: e_1_2_9_27_1 doi: 10.1142/S0219519418500380 – ident: e_1_2_9_24_1 doi: 10.1109/ACCESS.2021.3094720 – ident: e_1_2_9_28_1 doi: 10.14569/IJARAI.2015.040406 – ident: e_1_2_9_21_1 doi: 10.1186/s41747-020-00173-2 – ident: e_1_2_9_40_1 doi: 10.1007/s12098-020-03263-6 – ident: e_1_2_9_11_1 – ident: e_1_2_9_18_1 doi: 10.1016/j.patcog.2021.107828 – ident: e_1_2_9_5_1 doi: 10.1007/s00521-019-04650-7 – ident: e_1_2_9_2_1 doi: 10.1148/radiol.2020200642
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Snippet	The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID‐19, as per the World Health... The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health...
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SubjectTerms	Artificial neural networks Classification Coders Computed tomography COVID-19 COVID-19 Testing Deep learning Deeplabv3 denoise convolutional neural network (DnCNN) healthcare Humans Image enhancement Image segmentation Machine learning Medical imaging Neural networks Neural Networks, Computer public health ResNet‐18 SARS-CoV-2 Sputum stack sparse autoencoder deep learning model (SSAE) Tomography, X-Ray Computed
Title	Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network
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