20.7.2.3 Pneumonia, Lung Analysis, Flu, COVID

Chapter Contents (Back)
Pneumonia. COVID. Lungs. Medical, Applications.
See also GIS: GIS for Medical Applications, COVID Specific Tracking, Spread, Analysis.

Pattichis, M.S., Cacoullos, T., Soliz, P.[Peter],
New models for region of interest reader classification analysis in chest radiographs,
PR(42), No. 6, June 2009, pp. 1058-1066.
Elsevier DOI 0902
Region of interest classification; Chest radiographs; ROC analysis; Binary classification; Pneumoconiosis; Modeling biomedical systems; Logic, set theory, and algebra; Mathematical procedures and computer techniques BibRef

Zhao, W.[Wei], Xu, R.[Rui], Hirano, Y.S.[Yasu-Shi], Tachibana, R.[Rie], Kido, S.[Shoji], Suganuma, N.[Narufumi],
Classification of Pneumoconiosis on HRCT Images for Computer-Aided Diagnosis,
IEICE(E96-D), No. 4, April 2013, pp. 836-844.
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Wang, Y.[Ying], Waylen, P.R.[Peter R.], Mao, L.[Liang],
Modeling Properties of Influenza-Like Illness Peak Events with Crossing Theory,
IJGI(3), No. 2, 2014, pp. 764-780.
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Ouyang, X., Huo, J., Xia, L., Shan, F., Liu, J., Mo, Z., Yan, F., Ding, Z., Yang, Q., Song, B., Shi, F., Yuan, H., Wei, Y., Cao, X., Gao, Y., Wu, D., Wang, Q., Shen, D.,
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia,
MedImg(39), No. 8, August 2020, pp. 2595-2605.
IEEE DOI 2008
Lung, Computed tomography, Diseases, Hospitals, Radiology, Image segmentation, COVID-19, COVID-19 Diagnosis, Online Attention, Dual Sampling Strategy BibRef

Kang, H., Xia, L., Yan, F., Wan, Z., Shi, F., Yuan, H., Jiang, H., Wu, D., Sui, H., Zhang, C., Shen, D.,
Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning,
MedImg(39), No. 8, August 2020, pp. 2606-2614.
IEEE DOI 2008
Lung, Computed tomography, Feature extraction, Hospitals, Testing, COVID-19, COVID-19, Pneumonia, Chest computed tomography (CT), Multi-view representation learning BibRef

Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., Zheng, C.,
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT,
MedImg(39), No. 8, August 2020, pp. 2615-2625.
IEEE DOI 2008
Computed tomography, Lung, Lesions, Machine learning, Training, Diseases, COVID-19, COVID-19, CT, DeCoVNet BibRef

Fan, D., Zhou, T., Ji, G., Zhou, Y., Chen, G., Fu, H., Shen, J., Shao, L.,
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images,
MedImg(39), No. 8, August 2020, pp. 2626-2637.
IEEE DOI 2008
Computed tomography, Image segmentation, Lung, Training, Data models, Diseases, X-rays, COVID-19, COVID-19, CT image, infection segmentation, semi-supervised learning BibRef

Zhou, L., Li, Z., Zhou, J., Li, H., Chen, Y., Huang, Y., Xie, D., Zhao, L., Fan, M., Hashmi, S., Abdelkareem, F., Eiada, R., Xiao, X., Li, L., Qiu, Z., Gao, X.,
A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis,
MedImg(39), No. 8, August 2020, pp. 2638-2652.
IEEE DOI 2008
Computed tomography, Solid modeling, Lung, Image segmentation, COVID-19, COVID-19, computerized tomography BibRef

Wang, G., Liu, X., Li, C., Xu, Z., Ruan, J., Zhu, H., Meng, T., Li, K., Huang, N., Zhang, S.,
A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images,
MedImg(39), No. 8, August 2020, pp. 2653-2663.
IEEE DOI 2008
Noise measurement, Image segmentation, Lesions, Lung, Training, COVID-19, COVID-19, convolutional neural network, noisy label, pneumonia BibRef

Xie, W., Jacobs, C., Charbonnier, J., van Ginneken, B.,
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans,
MedImg(39), No. 8, August 2020, pp. 2664-2675.
IEEE DOI 2008
Computed tomography, Lung, Image segmentation, Diseases, Convolution, Neural networks, Training, COVID-19, Computed Tomography, COVID-19, Segmentation BibRef

Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Huijben, I., Chennakeshava, N., Mento, F., Sentelli, A., Peschiera, E., Trevisan, R., Maschietto, G., Torri, E., Inchingolo, R., Smargiassi, A., Soldati, G., Rota, P., Passerini, A., van Sloun, R.J.G., Ricci, E., Demi, L.,
Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound,
MedImg(39), No. 8, August 2020, pp. 2676-2687.
IEEE DOI 2008
Image segmentation, Lung, Ultrasonic imaging, Task analysis, Pathology, Imaging, Diseases, COVID-19, COVID-19, lung ultrasound, deep learning BibRef

Oh, Y., Park, S., Ye, J.C.,
Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets,
MedImg(39), No. 8, August 2020, pp. 2688-2700.
IEEE DOI 2008
Lung, Diseases, Image segmentation, Training, Neural networks, Sensitivity, Computed tomography, COVID-19, COVID-19, chest X-ray, saliency map BibRef

Seghier, M.L.[Mohamed L.],
The COVID-19 pandemic: What can bioengineers, computer scientists and big data specialists bring to the table,
IJIST(30), No. 3, 2020, pp. 511-512.
DOI Link 2008
BibRef

Han, Z., Wei, B., Hong, Y., Li, T., Cong, J., Zhu, X., Wei, H., Zhang, W.,
Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning,
MedImg(39), No. 8, August 2020, pp. 2584-2594.
IEEE DOI 2008
Computed tomography, Diseases, Lung, Manuals, Medical diagnostic imaging, machine learning BibRef

Wang, J., Bao, Y., Wen, Y., Lu, H., Luo, H., Xiang, Y., Li, X., Liu, C., Qian, D.,
Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images,
MedImg(39), No. 8, August 2020, pp. 2572-2583.
IEEE DOI 2008
Lung, Diseases, Computed tomography, Lesions, Task analysis, Image segmentation, Biomedical imaging, COVID-19, COVID-19, deep attention learning BibRef

Farhat, H.[Hanan], Sakr, G.E.[George E.], Kilany, R.[Rima],
Deep learning applications in pulmonary medical imaging: Recent updates and insights on COVID-19,
MVA(31), No. 6, August 2020, pp. Article53.
WWW Link. 2008
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Afshar, P.[Parnian], Heidarian, S.[Shahin], Naderkhani, F.[Farnoosh], Oikonomou, A.[Anastasia], Plataniotis, K.N.[Konstantinos N.], Mohammadi, A.[Arash],
COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images,
PRL(138), 2020, pp. 638-643.
Elsevier DOI 1806
COVID-19 pandemic, X-ray images, Deep learning, Capsule network BibRef

Wang, Z.[Zheng], Xiao, Y.[Ying], Li, Y.[Yong], Zhang, J.[Jie], Lu, F.G.[Fang-Gen], Hou, M.Z.[Mu-Zhou], Liu, X.W.[Xiao-Wei],
Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays,
PR(110), 2021, pp. 107613.
Elsevier DOI 2011
COVID-19, Computer-aided detection (CAD), Community-acquired pneumonia (CAP), Deep learning (DL), Chest X-ray (CXR) BibRef

Boulant, O.[Oliver], Fekom, M.[Mathilde], Pouchol, C.[Camille], Evgeniou, T.[Theodoros], Ovchinnikov, A.[Anton], Porcher, R.[Raphaël], Vayatis, N.[Nicolas],
SEAIR Framework Accounting for a Personalized Risk Prediction Score: Application to the Covid-19 Epidemic,
IPOL(10), 2020, pp. 150-166.
DOI Link 2011
BibRef

Polsinelli, M.[Matteo], Cinque, L.[Luigi], Placidi, G.[Giuseppe],
A light CNN for detecting COVID-19 from CT scans of the chest,
PRL(140), 2020, pp. 95-100.
Elsevier DOI 2012
Deep Learning, CNN, Pattern Recognition, COVID-19 BibRef

Sebastianelli, A.[Alessandro], Mauro, F.[Francesco], di Cosmo, G.[Gianluca], Passarini, F.[Fabrizio], Carminati, M.[Marco], Ullo, S.L.[Silvia Liberata],
AIRSENSE-TO-ACT: A Concept Paper for COVID-19 Countermeasures Based on Artificial Intelligence Algorithms and Multi-Source Data Processing,
IJGI(10), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Dey, N.[Nilanjan], Zhang, Y.D.[Yu-Dong], Rajinikanth, V., Pugalenthi, R., Raja, N.S.M.[N. Sri Madhava],
Customized VGG19 Architecture for Pneumonia Detection in Chest X-Rays,
PRL(143), 2021, pp. 67-74.
Elsevier DOI 2102
Chest X-Ray, Pneumonia, VGG19 Architecture, Deep-Learning, Ensemble Feature Scheme BibRef

Akgundogdu, A.[Abdurrahim],
Detection of pneumonia in chest X-ray images by using 2D discrete wavelet feature extraction with random forest,
IJIST(31), No. 1, 2021, pp. 82-93.
DOI Link 2102
image classification, machine learning, pneumonia, random forest, wavelet BibRef

Feng, J.B.[Jun-Bang], Guo, Y.[Yi], Wang, S.[Shike], Shi, F.[Feng], Wei, Y.[Ying], He, Y.[Yichu], Zeng, P.[Ping], Liu, J.[Jun], Wang, W.J.[Wen-Jing], Lin, L.P.[Li-Ping], Yang, Q.N.[Qing-Ning], Li, C.M.[Chuan-Ming], Liu, X.H.[Xing-Hua],
Differentiation between COVID-19 and bacterial pneumonia using radiomics of chest computed tomography and clinical features,
IJIST(31), No. 1, 2021, pp. 47-58.
DOI Link 2102
bacterial pneumonia, COVID-19, CT, LightGBM, radiomics BibRef

Öztürk, S.[Saban], Özkaya, U.[Umut], Barstugan, M.[Mücahid],
Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features,
IJIST(31), No. 1, 2021, pp. 5-15.
DOI Link 2102
classification, coronavirus, COVID-19, feature extraction, hand-crafted features, sAE BibRef

Zhou, T.X.[Tong-Xue], Canu, S.[Stéphane], Ruan, S.[Su],
Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism,
IJIST(31), No. 1, 2021, pp. 16-27.
DOI Link 2102
attention mechanism, COVID-19, CT, deep learning, focal tversky loss, segmentation BibRef

Selvaraj, D.[Deepika], Venkatesan, A.[Arunachalam], Mahesh, V.G.V.[Vijayalakshmi G. V.], Raj, A.N.J.[Alex Noel Joseph],
An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images,
IJIST(31), No. 1, 2021, pp. 28-46.
DOI Link 2102
artificial intelligence, computed tomography image, deep neural network, feature extraction, Zernike moment BibRef

Chen, Y., Zhang, H., Wang, Y., Yang, Y., Zhou, X., Wu, Q.M.J.,
MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection,
MedImg(40), No. 3, March 2021, pp. 1032-1041.
IEEE DOI 2103
COVID-19, Image reconstruction, Anomaly detection, Memory modules, Training, Feature extraction, Computed tomography, memory autoencoder BibRef

Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., Sun, Z., He, J., Li, Y., Shen, C., Xia, Y.,
Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection,
MedImg(40), No. 3, March 2021, pp. 879-890.
IEEE DOI 2103
Diseases, Lung, COVID-19, X-rays, Anomaly detection, Viruses (medical), Task analysis, Viral pneumonia screening, deep anomaly detection, chest X-ray BibRef

Wu, Y.H., Gao, S.H., Mei, J., Xu, J., Fan, D.P., Zhang, R.G., Cheng, M.M.,
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation,
IP(30), 2021, pp. 3113-3126.
IEEE DOI 2103
COVID-19, Computed tomography, Image segmentation, Sensitivity, X-rays, Pandemics, Lung, COVID-19, joint diagnosis, CT classification, COVID-19 dataset BibRef

Oulefki, A.[Adel], Agaian, S.[Sos], Trongtirakul, T.[Thaweesak], Kassah Laouar, A.[Azzeddine],
Automatic COVID-19 lung infected region segmentation and measurement using CT-scans images,
PR(114), 2021, pp. 107747.
Elsevier DOI 2103
Corona-virus Ddisease (COVID-19), Computer-Aaided Ddetection (CAD), COVID-19 lesion, 3D Visualization BibRef

Li, J.P.[Jin-Peng], Zhao, G.M.[Gang-Ming], Tao, Y.L.[Ya-Ling], Zhai, P.H.[Peng-Hua], Chen, H.[Hao], He, H.G.[Hui-Guang], Cai, T.[Ting],
Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19,
PR(114), 2021, pp. 107848.
Elsevier DOI 2103
Computed tomography, X-ray, COVID-19, Deep learning, Multi-task learning, Contrastive learning BibRef

Shorfuzzaman, M.[Mohammad], Hossain, M.S.[M. Shamim],
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients,
PR(113), 2021, pp. 107700.
Elsevier DOI 2103
COVID-19 diagnosis, Multi-shot learning, Contrastive loss, CXR images, Siamese network BibRef

Chen, X.[Xiaocong], Yao, L.[Lina], Zhou, T.[Tao], Dong, J.[Jinming], Zhang, Y.[Yu],
Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images,
PR(113), 2021, pp. 107826.
Elsevier DOI 2103
COVID-19 diagnosis, Few-shot learning, Contrastive learning, Chest CT images BibRef

He, K.[Kelei], Zhao, W.[Wei], Xie, X.Z.[Xing-Zhi], Ji, W.[Wen], Liu, M.X.[Ming-Xia], Tang, Z.Y.[Zhen-Yu], Shi, Y.H.[Ying-Huan], Shi, F.[Feng], Gao, Y.[Yang], Liu, J.[Jun], Zhang, J.F.[Jun-Feng], Shen, D.G.[Ding-Gang],
Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images,
PR(113), 2021, pp. 107828.
Elsevier DOI 2103
COVID-19, CT, Severity assessment, Lung lobe segmentation, Multi-instance learning BibRef

Samulowska, M.[Marta], Chmielewski, S.[Szymon], Raczko, E.[Edwin], Lupa, M.[Michal], Myszkowska, D.[Dorota], Zagajewski, B.[Bogdan],
Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping,
IJGI(10), No. 2, 2021, pp. xx-yy.
DOI Link 2103
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Upadhyay, K.[Kamini], Agrawal, M.[Monika], Deepak, D.[Desh],
Ensemble learning-based COVID-19 detection by feature boosting in chest X-ray images,
IET-IPR(14), No. 16, 19 December 2020, pp. 4059-4066.
DOI Link 2103
BibRef

Tiwari, S.[Shamik], Jain, A.[Anurag],
Convolutional capsule network for COVID-19 detection using radiography images,
IJIST(31), No. 2, 2021, pp. 525-539.
DOI Link 2105
capsule network, convolutional neural network, COVID-19, decision support system, deep learning, visual geometry group, X-ray BibRef

Polat, H.[Hasan], Özerdem, M.S.[Mehmet Siraç], Ekici, F.[Faysal], Akpolat, V.[Veysi],
Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks,
IJIST(31), No. 2, 2021, pp. 509-524.
DOI Link 2105
classification, computer-aided diagnosis, convolutional neural networks, coronavirus, COVID-19, radiology BibRef

Khan, M.A.[Murtaza Ali],
An automated and fast system to identify COVID-19 from X-ray radiograph of the chest using image processing and machine learning,
IJIST(31), No. 2, 2021, pp. 499-508.
DOI Link 2105
artificial intelligence, chest X-ray radiograph, COVID-19, feature descriptors, medical image processing BibRef

Dhaka, V.S.[Vijaypal Singh], Rani, G.[Geeta], Oza, M.G.[Meet Ganpatlal], Sharma, T.[Tarushi], Misra, A.[Ankit],
A deep learning model for mass screening of COVID-19,
IJIST(31), No. 2, 2021, pp. 483-498.
DOI Link 2105
CNN model, Corona, COVID-19, deep learning, global pandemic, X-ray BibRef

El-dosuky, M.A.[Mohamed A.], Soliman, M.[Mona], Hassanien, A.E.[Aboul Ella],
COVID-19 vs influenza viruses: A cockroach optimized deep neural network classification approach,
IJIST(31), No. 2, 2021, pp. 472-482.
DOI Link 2105
cockroach swarm optimization, convolutional neural networks, coronavirus, COVID-19, deep learning, influenza, SARS-CoV-2 BibRef

Dash, T.K.[Tusar Kanti], Mishra, S.[Soumya], Panda, G.[Ganapati], Satapathy, S.C.[Suresh Chandra],
Detection of COVID-19 from speech signal using bio-inspired based cepstral features,
PR(117), 2021, pp. 107999.
Elsevier DOI 2106
Bio-inspired computing, COVID19, Speech signal BibRef

Fan, Y.[Yuqi], Liu, J.H.[Jia-Hao], Yao, R.X.[Rui-Xuan], Yuan, X.H.[Xiao-Hui],
COVID-19 Detection from X-ray Images using Multi-Kernel-Size Spatial-Channel Attention Network,
PR(119), 2021, pp. 108055.
Elsevier DOI 2106
Deep learning, Attention, Coronavirus, X-ray images, Multi-scale BibRef

de Carvalho Brito, V.[Vitória], dos Santos, P.R.S.[Patrick Ryan Sales], de Sales Carvalho, N.R.[Nonato Rodrigues], de Carvalho Filho, A.O.[Antonio Oseas],
COVID-index: A texture-based approach to classifying lung lesions based on CT images,
PR(119), 2021, pp. 108083.
Elsevier DOI 2106
COVID-19, Computed tomography, 3D texture analysis, Phylogenetic diversity BibRef

Irmak, E.[Emrah],
COVID-19 disease severity assessment using CNN model,
IET-IPR(15), No. 8, 2021, pp. 1814-1824.
DOI Link 2106
BibRef

Koyuncu, H.[Hasan], Barstugan, M.[Mücahid],
COVID-19 discrimination framework for X-ray images by considering radiomics, selective information, feature ranking, and a novel hybrid classifier,
SP:IC(97), 2021, pp. 116359.
Elsevier DOI 2107
Binary categorization, Chaotic, Coronavirus, Framework design, Hybrid classifier, Optimization BibRef

Zhao, S.X.[Shi-Xuan], Li, Z.D.[Zhi-Dan], Chen, Y.[Yang], Zhao, W.[Wei], Xie, X.Z.[Xing-Zhi], Liu, J.[Jun], Zhao, D.[Di], Li, Y.J.[Yong-Jie],
SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images,
PR(119), 2021, pp. 108109.
Elsevier DOI 2108
COVID-19, Convolutional neural network, Segmentation, Lung opacification, Attention mechanism BibRef

Shaban, W.M.[Warda M.], Rabie, A.H.[Asmaa H.], Saleh, A.I.[Ahmed I.], Abo-Elsoud, M.A.,
Accurate detection of COVID-19 patients based on distance biased Naďve Bayes (DBNB) classification strategy,
PR(119), 2021, pp. 108110.
Elsevier DOI 2108
COVID-19, Classification, NB, Feature selection, Wrapper, Optimization, Particle swarm BibRef

Hu, J.[Jinlong], Xu, S.[Songhua], Ding, X.D.[Xiang-Dong],
A Triplet network framework based automatic assessment of simulation quality for respiratory droplet propagation,
PR(119), 2021, pp. 108060.
Elsevier DOI 2108
Simulation quality assessment, Respiratory droplet propagation, Triplet network, Attentive temporal pooling BibRef

Vieira, P.[Pablo], Sousa, O.[Orrana], Magalhăes, D.[Deborah], Rabęlo, R.[Ricardo], Silva, R.[Romuere],
Detecting pulmonary diseases using deep features in X-ray images,
PR(119), 2021, pp. 108081.
Elsevier DOI 2108
COVID-19, X-ray, Deep learning, Pre-processing BibRef

Zhao, C.[Chen], Xu, Y.[Yan], He, Z.[Zhuo], Tang, J.S.[Jin-Shan], Zhang, Y.J.[Yi-Jun], Han, J.G.[Jun-Gang], Shi, Y.X.[Yu-Xin], Zhou, W.H.[Wei-Hua],
Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images,
PR(119), 2021, pp. 108071.
Elsevier DOI 2108
COVID-19, Chest CT, Pulmonary parenchyma segmentation, Deep learning, 3D V-Net BibRef

Cao, W.[Wen], Dai, H.[Haoran], Zhu, J.W.[Jing-Wen], Tian, Y.[Yuzhen], Peng, F.[Feilin],
Analysis and Evaluation of Non-Pharmaceutical Interventions on Prevention and Control of COVID-19: A Case Study of Wuhan City,
IJGI(10), No. 7, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Cho, Y.[Yongwon], Hwang, S.H.[Sung Ho], Oh, Y.W.[Yu-Whan], Ham, B.J.[Byung-Joo], Kim, M.J.[Min Ju], Park, B.J.[Beom Jin],
Deep convolution neural networks to differentiate between COVID-19 and other pulmonary abnormalities on chest radiographs: Evaluation using internal and external datasets,
IJIST(31), No. 3, 2021, pp. 1087-1104.
DOI Link 2108
chest radiography, computer-aided diagnosis (CAD), COVID-19, deep learning, lung diseases BibRef

Johri, S.[Shikhar], Goyal, M.[Mehendi], Jain, S.[Sahil], Baranwal, M.[Manoj], Kumar, V.[Vinay], Upadhyay, R.[Rahul],
A novel machine learning-based analytical framework for automatic detection of COVID-19 using chest X-ray images,
IJIST(31), No. 3, 2021, pp. 1105-1119.
DOI Link 2108
chest X-ray images, coronavirus, machine learning methods, pneumonia BibRef

Tang, L.[Lu], Tian, C.[Chuangeng], Meng, Y.[Yankai], Xu, K.[Kai],
Longitudinal evaluation for COVID-19 chest CT disease progression based on Tchebichef moments,
IJIST(31), No. 3, 2021, pp. 1120-1127.
DOI Link 2108
blur, COVID-19 CT image, disease progression, objective evaluation, Tchebichef moments BibRef

Zhang, X.[XiaoQing], Wang, G.[GuangYu], Zhao, S.G.[Shu-Guang],
COVSeg-NET: A deep convolution neural network for COVID-19 lung CT image segmentation,
IJIST(31), No. 3, 2021, pp. 1071-1086.
DOI Link 2108
convolution neural network, COVID-19, image segmentation, lung CT image BibRef

Yu, F.[Fuli], Zhu, Y.[Yu], Qin, X.X.[Xiang-Xiang], Xin, Y.[Ying], Yang, D.[Dawei], Xu, T.[Tao],
A multi-class COVID-19 segmentation network with pyramid attention and edge loss in CT images,
IET-IPR(15), No. 11, 2021, pp. 2604-2613.
DOI Link 2108
BibRef

Mohammadi, A.[Arash], Wang, Y.X.[Ying-Xu], Enshaei, N.[Nastaran], Afshar, P.[Parnian], Naderkhani, F.[Farnoosh], Oikonomou, A.[Anastasia], Rafiee, J.[Javad], Rodrigues de Oliveira, H.[Helder], Yanushkevich, S.[Svetlana], Plataniotis, K.N.[Konstantinos N.],
Diagnosis/Prognosis of COVID-19 Chest Images via Machine Learning and Hypersignal Processing: Challenges, opportunities, and applications,
SPMag(38), No. 5, September 2021, pp. 37-66.
IEEE DOI 2109
COVID-19, Deep learning, Pandemics, Signal processing, Prognostics and health management, Epidemiology, Monitoring BibRef

Aversano, L.[Lerina], Bernardi, M.L.[Mario Luca], Cimitile, M.[Marta], Pecori, R.[Riccardo],
Deep neural networks ensemble to detect COVID-19 from CT scans,
PR(120), 2021, pp. 108135.
Elsevier DOI 2109
Deep learning, CT Scan images, COVID-19, Coronavirus BibRef

Mu, N.[Nan], Wang, H.Y.[Hong-Yu], Zhang, Y.[Yu], Jiang, J.F.[Jing-Feng], Tang, J.S.[Jin-Shan],
Progressive global perception and local polishing network for lung infection segmentation of COVID-19 CT images,
PR(120), 2021, pp. 108168.
Elsevier DOI 2109
Coronavirus disease 2019 (COVID-19), Global perception, Local polishing, Feature recursive aggregation, Multiple supervision BibRef

Wang, X.F.[Xiao-Fei], Jiang, L.[Lai], Li, L.[Liu], Xu, M.[Mai], Deng, X.[Xin], Dai, L.[Lisong], Xu, X.Y.[Xiang-Yang], Li, T.[Tianyi], Guo, Y.[Yichen], Wang, Z.[Zulin], Dragotti, P.L.[Pier Luigi],
Joint Learning of 3D Lesion Segmentation and Classification for Explainable COVID-19 Diagnosis,
MedImg(40), No. 9, September 2021, pp. 2463-2476.
IEEE DOI 2109
Lesions, COVID-19, Computed tomography, Task analysis, Databases, Biological system modeling, deep neural networks BibRef

Zhang, Y.D.[Yu-Dong], Zhang, Z.[Zheng], Zhang, X.[Xin], Wang, S.H.[Shui-Hua],
MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray,
PRL(150), 2021, pp. 8-16.
Elsevier DOI 2109
Deep learning, Data harmonization, Multiple input, Convolutional neural network, Automatic differentiation, Multimodality BibRef

Guarrasi, V.[Valerio], D'Amico, N.C.[Natascha Claudia], Sicilia, R.[Rosa], Cordelli, E.[Ermanno], Soda, P.[Paolo],
Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays,
PR(121), 2022, pp. 108242.
Elsevier DOI 2109
COVID-19, X-ray, Deep-learning, Multi-expert systems, Optimization, Convolutional neural networks BibRef

Mansour, R.F.[Romany F.], Escorcia-Gutierrez, J.[José], Gamarra, M.[Margarita], Gupta, D.[Deepak], Castillo, O.[Oscar], Kumar, S.[Sachin],
Unsupervised Deep Learning based Variational Autoencoder Model for COVID-19 Diagnosis and Classification,
PRL(151), 2021, pp. 267-274.
Elsevier DOI 2110
COVID-19, Deep learning, Unsupervised learning, Variational autoencoder, Image classification BibRef

Chen, J.G.[Jian-Guo], Li, K.[Kenli], Zhang, Z.[Zhaolei], Li, K.Q.[Ke-Qin], Yu, P.S.[Philip S.],
A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19,
Surveys(54), No. 8, October 2021, pp. xx-yy.
DOI Link 2110
SARS-CoV-2, COVID-19, Artificial intelligence BibRef

Yao, Q.S.[Qing-Song], Xiao, L.[Li], Liu, P.[Peihang], Zhou, S.K.[S. Kevin],
Label-Free Segmentation of COVID-19 Lesions in Lung CT,
MedImg(40), No. 10, October 2021, pp. 2808-2819.
IEEE DOI 2110
Lesions, COVID-19, Computed tomography, Lung, Image segmentation, Training, Task analysis, COVID-19, label-free lesion segmentation, voxel-level anomaly modeling BibRef


Simon, M.[Mylene], Schaub, N.J.[Nicholas J.], Yu, S.[Sunny], Ouladi, M.[Mohamed], Nagarajan, J.[Jayapriya], Bayankaram, S.P.[Sudharsan Prativadi], Bajcsy, P.[Peter], Hotaling, N.[Nathan],
Quantifying Variability in Microscopy Image Analyses for COVID-19 Drug Discovery,
CVMI21(3796-3804)
IEEE DOI 2109
Drugs, COVID-19, Coordinate measuring machines, Thresholding (Imaging), Microscopy, Measurement uncertainty, Hardware BibRef

Campos, M.S.R.[Michael Stiven Ramirez], Bautista, S.S.[Santiago Saavedra], Guerrero, J.V.A.[Jose Vicente Alzate], Suárez, S.C.[Sandra Cancino], López, J.M.L.[Juan M. López],
COVID-19 Related Pneumonia Detection in Lung Ultrasound,
MCPR21(316-324).
Springer DOI 2108
BibRef

Laradji, I.[Issam], Rodriguez, P.[Pau], Mańas, O.[Oscar], Lensink, K.[Keegan], Law, M.[Marco], Kurzman, L.[Lironne], Parker, W.[William], Vázquez, D.[David], Nowrouzezahrai, D.[Derek],
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images,
WACV21(2452-2461)
IEEE DOI 2106
COVID-19, Learning systems, Image segmentation, Annotations, Computed tomography BibRef

Calderon-Ramirez, S.[Saul], Giri, R.[Raghvendra], Yang, S.X.[Sheng-Xiang], Moemeni, A.[Armaghan], Umańa, M.[Mario], Elizondo, D.[David], Torrents-Barrena, J.[Jordina], Molina-Cabello, M.A.[Miguel A.],
Dealing with Scarce Labelled Data: Semi-supervised Deep Learning with Mix Match for Covid-19 Detection Using Chest X-ray Images,
ICPR21(5294-5301)
IEEE DOI 2105
Deep learning, COVID-19, Training, Solid modeling, Scalability, X-rays, Semisupervised learning, Semi-supervised Deep Learning, Computer Aided Diagnosis BibRef

Prasad, S.[Shitala], Lin, D.[Dongyun], Li, Y.Q.[Yi-Qun], Sheng, D.[Dong], Min, O.Z.[Oo Zaw],
Rethinking of Deep Models Parameters with Respect to Data Distribution,
ICPR21(8562-8569)
IEEE DOI 2105
Training, Deep learning, COVID-19, Image segmentation, Annotations, Object detection, Data models BibRef

Xu, R.[Rui], Wang, Y.[Yi], Liu, T.T.[Tian-Tian], Ye, X.[Xinchen], Lin, L.[Lin], Chen, Y.W.[Yen-Wei], Kido, S.[Shoji], Tomiyama, N.[Noriyuki],
BG-Net: Boundary-Guided Network for Lung Segmentation on Clinical CT Images,
ICPR21(8782-8788)
IEEE DOI 2105
COVID-19, Deep learning, Image segmentation, Hospitals, Computed tomography, Pulmonary diseases, Lung, Lung Segmentation, CT Images BibRef

Xu, R.[Rui], Cao, X.[Xiao], Wang, Y.F.[Yu-Feng], Chen, Y.W.[Yen-Wei], Ye, X.C.[Xin-Chen], Lin, L.[Lin], Zhu, W.C.[Wen-Chao], Chen, C.[Chao], Xu, F.[Fangyi], Zhou, Y.[Yong], Hu, H.J.[Hong-Jie], Kido, S.[Shoji], Tomiyama, N.[Noriyuki],
Unsupervised Detection of Pulmonary Opacities for Computer-Aided Diagnosis of COVID-19 on CT Images,
ICPR21(9007-9014)
IEEE DOI 2105
COVID-19, Solid modeling, Computed tomography, Pulmonary diseases, Lung, Tools, Feature extraction, COVID-19, CT images, Computer-aided diagnosis BibRef

Buongiorno, R.[Rossana], Germanese, D.[Danila], Romei, C.[Chiara], Tavanti, L.[Laura], de Liperi, A.[Annalisa], Colantonio, S.[Sara],
UIP-Net: A Decoder-encoder CNN for the Detection and Quantification of Usual Interstitial Pneumoniae Pattern in Lung CT Scan Images,
AIHA20(389-405).
Springer DOI 2103
BibRef

Yazdekhasty, P.[Parham], Zindari, A.[Ali], Nabizadeh-ShahreBabak, Z.[Zahra], Roshandel, R.[Roshanak], Khadivi, P.[Pejman], Karimi, N.[Nader], Samavi, S.[Shadrokh],
Bifurcated Autoencoder for Segmentation of Covid-19 Infected Regions in Ct Images,
DLPR20(597-607).
Springer DOI 2103
BibRef

Chiari, M.[Mattia], Gerevini, A.E.[Alfonso E.], Maroldi, R.[Roberto], Olivato, M.[Matteo], Putelli, L.[Luca], Serina, I.[Ivan],
Length of Stay Prediction for Northern Italy Covid-19 Patients Based on Lab Tests and X-ray Data,
AIHA20(212-226).
Springer DOI 2103
BibRef

Qjidaa, M., Ben-Fares, A., Mechbal, Y., Amakdouf, H., Maaroufi, M., Alami, B., Qjidaa, H.,
Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images,
ISCV20(1-6)
IEEE DOI 2011
convolutional neural nets, decision support systems, diagnostic radiography, diseases, feature extraction, clinical decision support system BibRef

Qjidaa, M., Mechbal, Y., Ben-fares, A., Amakdouf, H., Maaroufi, M., Alami, B., Qjidaa, H.,
Early detection of COVID19 by deep learning transfer Model for populations in isolated rural areas,
ISCV20(1-5)
IEEE DOI 2011
decision support systems, diagnostic radiography, diseases, image classification, learning (artificial intelligence), lung, rural area BibRef

Gazzah, S., Bencharef, O.,
A Survey on how computer vision can response to urgent need to contribute in COVID-19 pandemics,
ISCV20(1-5)
IEEE DOI 2011
computer vision, computerised tomography, diagnostic radiography, diseases, epidemics, learning (artificial intelligence), CNN. BibRef

Gabruseva, T., Poplavskiy, D., Kalinin, A.,
Deep Learning for Automatic Pneumonia Detection,
WiCV20(1436-1443)
IEEE DOI 2008
Lung, Diseases, Training, X-ray imaging, Diagnostic radiography, Measurement, Predictive models BibRef

Sousa, G.G.B.[Gabriel Garcez Barros], Fernandes, V.R.M.[Vandécia Rejane Monteiro], de Paiva, A.C.[Anselmo Cardoso],
Optimized Deep Learning Architecture for the Diagnosis of Pneumonia Through Chest X-Rays,
ICIAR19(II:353-361).
Springer DOI 1909
BibRef

Murray, V.[Victor], Pattichis, M.S.[Marios S.], Davis, H.[Herbert], Barriga, E.S.[Eduardo S.], Soliz, P.[Peter],
Multiscale AM-FM analysis of pneumoconiosis x-ray images,
ICIP09(4201-4204).
IEEE DOI 0911
BibRef

Tong, X.[Xiaoou], Tao, D.C.[Da-Cheng], Antonio, G.E.,
Texture classification of SARS infected region in radiographic image,
ICIP04(V: 2941-2944).
IEEE DOI 0505
BibRef

Pattichis, M.S., Pattichis, C.S., Christodoulou, C.I., James, D., Ketai, L., Soliz, P.,
A screening system for the assessment of opacity profusion in chest radiographs of miners with pneumoconiosis,
Southwest02(130-133).
IEEE Top Reference. 0208
BibRef

Chen, X.[Xuan], Hasegawa, J.I., Toriwaki, J.I.,
Quantitative diagnosis of pneumoconiosis based on recognition of small rounded opacities in chest X-ray images,
ICPR88(I: 462-464).
IEEE DOI 8811
BibRef

Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Emphysema, Lung Analysis .


Last update:Nov 1, 2021 at 09:26:50