Pneumonia, Lung Analysis, Flu, COVID

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Pneumonia. COVID. Lungs. Medical, Applications.

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.
WWW Link. 1304

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.
DOI Link 1407

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.
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.
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.
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.
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.
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.
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.
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.
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.
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

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.
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.
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

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

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

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.[Chuanming], 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.
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.
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.
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

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

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

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,
Springer DOI 2103

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,
Springer DOI 2103

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,
Springer DOI 2103

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,
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,
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,
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,
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,
Springer DOI 1909

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,

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

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,
IEEE Top Reference. 0208

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).

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

Last update:May 10, 2021 at 18:51:10