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For: Ko H, Chung H, Kang WS, Kim KW, Shin Y, Kang SJ, Lee JH, Kim YJ, Kim NY, Jung H, Lee J. COVID-19 Pneumonia Diagnosis Using a Simple 2D Deep Learning Framework With a Single Chest CT Image: Model Development and Validation. J Med Internet Res 2020;22:e19569. [PMID: 32568730 DOI: 10.2196/19569] [Cited by in Crossref: 52] [Cited by in F6Publishing: 37] [Article Influence: 26.0] [Reference Citation Analysis]
Number Citing Articles
1 Hassan H, Ren Z, Zhou C, Khan MA, Pan Y, Zhao J, Huang B. Supervised and Weakly Supervised Deep Learning Models for COVID-19 CT Diagnosis: A Systematic Review. Computer Methods and Programs in Biomedicine 2022. [DOI: 10.1016/j.cmpb.2022.106731] [Reference Citation Analysis]
2 Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Front Med (Lausanne) 2021;8:704256. [PMID: 34660623 DOI: 10.3389/fmed.2021.704256] [Reference Citation Analysis]
3 Karthik R, Menaka R, Hariharan M, Kathiresan GS. AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions. Ing Rech Biomed 2021. [PMID: 34336141 DOI: 10.1016/j.irbm.2021.07.002] [Reference Citation Analysis]
4 Min Kim H, Ko T, Young Choi I, Myong JP. Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image. Int J Med Inform 2021;158:104667. [PMID: 34952282 DOI: 10.1016/j.ijmedinf.2021.104667] [Reference Citation Analysis]
5 Kato S, Ishiwata Y, Aoki R, Iwasawa T, Hagiwara E, Ogura T, Utsunomiya D. Imaging of COVID-19: An update of current evidences. Diagn Interv Imaging 2021;102:493-500. [PMID: 34088635 DOI: 10.1016/j.diii.2021.05.006] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Aria M, Nourani E, Golzari Oskouei A, Liu J. ADA-COVID: Adversarial Deep Domain Adaptation-Based Diagnosis of COVID-19 from Lung CT Scans Using Triplet Embeddings. Computational Intelligence and Neuroscience 2022;2022:1-17. [DOI: 10.1155/2022/2564022] [Reference Citation Analysis]
7 Xu M, Ouyang L, Han L, Sun K, Yu T, Li Q, Tian H, Safarnejad L, Zhang H, Gao Y, Bao FS, Chen Y, Robinson P, Ge Y, Zhu B, Liu J, Chen S. Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach. J Med Internet Res 2021;23:e25535. [PMID: 33404516 DOI: 10.2196/25535] [Cited by in Crossref: 4] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
8 [DOI: 10.1101/2020.08.18.20176776] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
9 Shankar K, Perumal E, Díaz VG, Tiwari P, Gupta D, Saudagar AKJ, Muhammad K. An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images. Appl Soft Comput 2021;113:107878. [PMID: 34512217 DOI: 10.1016/j.asoc.2021.107878] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
10 Ghaderzadeh M, Asadi F. Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review. J Healthc Eng 2021;2021:6677314. [PMID: 33747419 DOI: 10.1155/2021/6677314] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 9.0] [Reference Citation Analysis]
11 Helwan A, Ma'aitah MKS, Hamdan H, Ozsahin DU, Tuncyurek O. Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19. Comput Math Methods Med 2021;2021:5527271. [PMID: 34055034 DOI: 10.1155/2021/5527271] [Reference Citation Analysis]
12 Moezzi M, Shirbandi K, Shahvandi HK, Arjmand B, Rahim F. The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis. Inform Med Unlocked 2021;24:100591. [PMID: 33977119 DOI: 10.1016/j.imu.2021.100591] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
13 Owais M, Arsalan M, Mahmood T, Kang JK, Park KR. Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning-Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation. J Med Internet Res 2020;22:e18563. [PMID: 33242010 DOI: 10.2196/18563] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Ding W, Nayak J, Swapnarekha H, Abraham A, Naik B, Pelusi D. Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data. Neurocomputing 2021;457:40-66. [PMID: 34149184 DOI: 10.1016/j.neucom.2021.06.024] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, Aviles-rivero AI, Etmann C, Mccague C, Beer L, Weir-mccall JR, Teng Z, Gkrania-klotsas E, Rudd JHF, Sala E, Schönlieb C; AIX-COVNET. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 2021;3:199-217. [DOI: 10.1038/s42256-021-00307-0] [Cited by in Crossref: 65] [Cited by in F6Publishing: 13] [Article Influence: 65.0] [Reference Citation Analysis]
16 Nagaoka T, Kozuka T, Yamada T, Habe H, Nemoto M, Tada M, Abe K, Handa H, Yoshida H, Ishii K, Kimura Y. A Deep Learning System to Diagnose COVID-19 Pneumonia Using Masked Lung CT Images to Avoid AI-generated COVID-19 Diagnoses that Include Data outside the Lungs. ABE 2022;11:76-86. [DOI: 10.14326/abe.11.76] [Reference Citation Analysis]
17 Wang SH, Nayak DR, Guttery DS, Zhang X, Zhang YD. COVID-19 classification by CCSHNet with deep fusion using transfer learning and discriminant correlation analysis. Inf Fusion 2021;68:131-48. [PMID: 33519321 DOI: 10.1016/j.inffus.2020.11.005] [Cited by in Crossref: 29] [Cited by in F6Publishing: 11] [Article Influence: 14.5] [Reference Citation Analysis]
18 Wang SH, Zhang Y, Cheng X, Zhang X, Zhang YD. PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation. Comput Math Methods Med 2021;2021:6633755. [PMID: 33777167 DOI: 10.1155/2021/6633755] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
19 Wang SH, Satapathy SC, Anderson D, Chen SX, Zhang YD. Deep Fractional Max Pooling Neural Network for COVID-19 Recognition. Front Public Health 2021;9:726144. [PMID: 34447739 DOI: 10.3389/fpubh.2021.726144] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Senan EM, Alzahrani A, Alzahrani MY, Alsharif N, Aldhyani THH. Automated Diagnosis of Chest X-Ray for Early Detection of COVID-19 Disease. Comput Math Methods Med 2021;2021:6919483. [PMID: 34721659 DOI: 10.1155/2021/6919483] [Reference Citation Analysis]
21 Mulrenan C, Rhode K, Fischer BM. A Literature Review on the Use of Artificial Intelligence for the Diagnosis of COVID-19 on CT and Chest X-ray. Diagnostics 2022;12:869. [DOI: 10.3390/diagnostics12040869] [Reference Citation Analysis]
22 Liu J, Sun W, Zhao X, Zhao J, Jiang Z. Deep feature fusion classification network (DFFCNet): Towards accurate diagnosis of COVID-19 using chest X-rays images. Biomedical Signal Processing and Control 2022;76:103677. [DOI: 10.1016/j.bspc.2022.103677] [Reference Citation Analysis]
23 Santosh KC, Ghosh S. Covid-19 Imaging Tools: How Big Data is Big? J Med Syst 2021;45:71. [PMID: 34081193 DOI: 10.1007/s10916-021-01747-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
24 Fallahpoor M, Chakraborty S, Heshejin MT, Chegeni H, Horry MJ, Pradhan B. Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection. Comput Biol Med 2022;145:105464. [PMID: 35390746 DOI: 10.1016/j.compbiomed.2022.105464] [Reference Citation Analysis]
25 Hasan MK, Jawad MT, Hasan KNI, Partha SB, Masba MMA, Saha S, Moni MA. COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancing. Inform Med Unlocked 2021;26:100709. [PMID: 34642640 DOI: 10.1016/j.imu.2021.100709] [Reference Citation Analysis]
26 Rehouma R, Buchert M, Chen YP. Machine learning for medical imaging‐based COVID‐19 detection and diagnosis. Int J Intell Syst 2021;36:5085-115. [DOI: 10.1002/int.22504] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
27 Ahuja V, Nair LV. Artificial Intelligence and technology in COVID Era: A narrative review. J Anaesthesiol Clin Pharmacol 2021;37:28-34. [PMID: 34103818 DOI: 10.4103/joacp.JOACP_558_20] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Wang Y, Tsai D, Yen L, Yao Y, Chiang T, Chiu C, Lin T, Yeh K, Chang F. Clinical Characteristics of COVID-19 Patients and Application to an Artificial Intelligence System for Disease Surveillance. JCM 2022;11:1437. [DOI: 10.3390/jcm11051437] [Reference Citation Analysis]
29 Ho TT, Park J, Kim T, Park B, Lee J, Kim JY, Kim KB, Choi S, Kim YH, Lim JK, Choi S. Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study. JMIR Med Inform 2021;9:e24973. [PMID: 33455900 DOI: 10.2196/24973] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
30 Fusco R, Grassi R, Granata V, Setola SV, Grassi F, Cozzi D, Pecori B, Izzo F, Petrillo A. Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment. J Pers Med 2021;11:993. [PMID: 34683133 DOI: 10.3390/jpm11100993] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Hasan N. A Hybrid Method of Covid-19 Patient Detection from Modified CT-Scan/Chest-X-Ray Images Combining Deep Convolutional Neural Network And Two- Dimensional Empirical Mode Decomposition. Comput Methods Programs Biomed Update 2021;1:100022. [PMID: 34337590 DOI: 10.1016/j.cmpbup.2021.100022] [Reference Citation Analysis]
32 van der Velden BH, Kuijf HJ, Gilhuijs KG, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Medical Image Analysis 2022;79:102470. [DOI: 10.1016/j.media.2022.102470] [Reference Citation Analysis]
33 Tayarani N MH. Applications of artificial intelligence in battling against covid-19: A literature review. Chaos Solitons Fractals 2021;142:110338. [PMID: 33041533 DOI: 10.1016/j.chaos.2020.110338] [Cited by in Crossref: 28] [Cited by in F6Publishing: 13] [Article Influence: 14.0] [Reference Citation Analysis]
34 Ho TT, Kim GT, Kim T, Choi S, Park EK. Classification of rotator cuff tears in ultrasound images using deep learning models. Med Biol Eng Comput 2022. [PMID: 35043367 DOI: 10.1007/s11517-022-02502-6] [Reference Citation Analysis]
35 Shi W, Peng X, Liu T, Cheng Z, Lu H, Yang S, Zhang J, Wang M, Gao Y, Shi Y, Zhang Z, Shan F. A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients. Ann Transl Med 2021;9:216. [PMID: 33708843 DOI: 10.21037/atm-20-2464] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 8.0] [Reference Citation Analysis]
36 Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Comput Struct Biotechnol J 2021;19:2833-50. [PMID: 34025952 DOI: 10.1016/j.csbj.2021.05.010] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
37 Dey S, Bhattacharya R, Malakar S, Mirjalili S, Sarkar R. Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection. Comput Biol Med 2021;135:104585. [PMID: 34229144 DOI: 10.1016/j.compbiomed.2021.104585] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Li D, Zhang Q, Tan Y, Feng X, Yue Y, Bai Y, Li J, Li J, Xu Y, Chen S, Xiao SY, Sun M, Li X, Zhu F. Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach. JMIR Med Inform 2020;8:e21604. [PMID: 33038076 DOI: 10.2196/21604] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
39 Javidi M, Abbaasi S, Naybandi Atashi S, Jampour M. COVID-19 early detection for imbalanced or low number of data using a regularized cost-sensitive CapsNet. Sci Rep 2021;11:18478. [PMID: 34531477 DOI: 10.1038/s41598-021-97901-4] [Reference Citation Analysis]
40 Glangetas A, Hartley MA, Cantais A, Courvoisier DS, Rivollet D, Shama DM, Perez A, Spechbach H, Trombert V, Bourquin S, Jaggi M, Barazzone-Argiroffo C, Gervaix A, Siebert JN. Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study. BMC Pulm Med 2021;21:103. [PMID: 33761909 DOI: 10.1186/s12890-021-01467-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
41 Yang L, Wang SH, Zhang YD. EDNC: Ensemble Deep Neural Network for COVID-19 Recognition. Tomography 2022;8:869-90. [PMID: 35314648 DOI: 10.3390/tomography8020071] [Reference Citation Analysis]
42 Athavale AM, Hart PD, Itteera M, Cimbaluk D, Patel T, Alabkaa A, Arruda J, Singh A, Rosenberg A, Kulkarni H. Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images. JAMA Netw Open 2021;4:e2111176. [PMID: 34028548 DOI: 10.1001/jamanetworkopen.2021.11176] [Reference Citation Analysis]
43 Negrini D, Danese E, Henry BM, Lippi G, Montagnana M. Artificial intelligence at the time of COVID-19: who does the lion's share? Clin Chem Lab Med 2022. [PMID: 35470639 DOI: 10.1515/cclm-2022-0306] [Reference Citation Analysis]
44 Ishiwata Y, Miura K, Kishimoto M, Nomura K, Sawamura S, Magami S, Ikawa M, Yamashiro T, Utsunomiya D. Comparison of CO-RADS Scores Based on Visual and Artificial Intelligence Assessments in a Non-Endemic Area. Diagnostics 2022;12:738. [DOI: 10.3390/diagnostics12030738] [Reference Citation Analysis]
45 Mohammad-Rahimi H, Nadimi M, Ghalyanchi-Langeroudi A, Taheri M, Ghafouri-Fard S. Application of Machine Learning in Diagnosis of COVID-19 Through X-Ray and CT Images: A Scoping Review. Front Cardiovasc Med 2021;8:638011. [PMID: 33842563 DOI: 10.3389/fcvm.2021.638011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
46 Sitaula C, Hossain MB. Attention-based VGG-16 model for COVID-19 chest X-ray image classification. Appl Intell (Dordr) 2020;:1-14. [PMID: 34764568 DOI: 10.1007/s10489-020-02055-x] [Cited by in Crossref: 18] [Article Influence: 9.0] [Reference Citation Analysis]
47 Wang SH, Govindaraj VV, Górriz JM, Zhang X, Zhang YD. Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inf Fusion 2021;67:208-29. [PMID: 33052196 DOI: 10.1016/j.inffus.2020.10.004] [Cited by in Crossref: 48] [Cited by in F6Publishing: 16] [Article Influence: 24.0] [Reference Citation Analysis]
48 Shiri I, Arabi H, Salimi Y, Sanaat A, Akhavanallaf A, Hajianfar G, Askari D, Moradi S, Mansouri Z, Pakbin M, Sandoughdaran S, Abdollahi H, Radmard AR, Rezaei-Kalantari K, Ghelich Oghli M, Zaidi H. COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. Int J Imaging Syst Technol 2021. [PMID: 34898850 DOI: 10.1002/ima.22672] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 Abbasi WA, Abbas SA, Andleeb S, Ul Islam G, Ajaz SA, Arshad K, Khalil S, Anjam A, Ilyas K, Saleem M, Chughtai J, Abbas A. COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology. Inform Med Unlocked 2021;23:100540. [PMID: 33644298 DOI: 10.1016/j.imu.2021.100540] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
50 Ozsahin I, Sekeroglu B, Musa MS, Mustapha MT, Uzun Ozsahin D. Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence. Comput Math Methods Med 2020;2020:9756518. [PMID: 33014121 DOI: 10.1155/2020/9756518] [Cited by in Crossref: 31] [Cited by in F6Publishing: 23] [Article Influence: 15.5] [Reference Citation Analysis]
51 Rasheed J, Jamil A, Hameed AA, Al-Turjman F, Rasheed A. COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review. Interdiscip Sci 2021;13:153-75. [PMID: 33886097 DOI: 10.1007/s12539-021-00431-w] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
52 Han CH, Kim M, Kwak JT. Semi-supervised learning for an improved diagnosis of COVID-19 in CT images. PLoS One 2021;16:e0249450. [PMID: 33793650 DOI: 10.1371/journal.pone.0249450] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
53 Kumar V, Singh D, Kaur M, Damaševičius R. Overview of current state of research on the application of artificial intelligence techniques for COVID-19. PeerJ Comput Sci 2021;7:e564. [PMID: 34141890 DOI: 10.7717/peerj-cs.564] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
54 Chung H, Ko H, Kang WS, Kim KW, Lee H, Park C, Song HO, Choi TY, Seo JH, Lee J. Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation. J Med Internet Res 2021;23:e27060. [PMID: 33764883 DOI: 10.2196/27060] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
55 Alhasan M, Hasaneen M. Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic. Comput Med Imaging Graph 2021;91:101933. [PMID: 34082281 DOI: 10.1016/j.compmedimag.2021.101933] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
56 Park S, Ko T, Park C, Kim Y, Choi I. Deep Learning Model Based on 3D Optical Coherence Tomography Images for the Automated Detection of Pathologic Myopia. Diagnostics 2022;12:742. [DOI: 10.3390/diagnostics12030742] [Reference Citation Analysis]
57 Rahman MM, Nooruddin S, Hasan KMA, Dey NK. HOG + CNN Net: Diagnosing COVID-19 and Pneumonia by Deep Neural Network from Chest X-Ray Images. SN Comput Sci 2021;2:371. [PMID: 34254055 DOI: 10.1007/s42979-021-00762-x] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
58 Liu Q, Pang B, Li H, Zhang B, Liu Y, Lai L, Le W, Li J, Xia T, Zhang X, Ou C, Ma J, Li S, Guo X, Zhang S, Zhang Q, Jiang M, Zeng Q. Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia. J Thorac Dis 2021;13:1215-29. [PMID: 33717594 DOI: 10.21037/jtd-20-2580] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
59 Homayounieh F, Bezerra Cavalcanti Rockenbach MA, Ebrahimian S, Doda Khera R, Bizzo BC, Buch V, Babaei R, Karimi Mobin H, Mohseni I, Mitschke M, Zimmermann M, Durlak F, Rauch F, Digumarthy SR, Kalra MK. Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome. J Digit Imaging 2021;34:320-9. [PMID: 33634416 DOI: 10.1007/s10278-021-00430-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
60 Manokaran J, Zabihollahy F, Hamilton-Wright A, Ukwatta E. Detection of COVID-19 from chest x-ray images using transfer learning. J Med Imaging (Bellingham) 2021;8:017503. [PMID: 34435075 DOI: 10.1117/1.JMI.8.S1.017503] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
61 Syeda HB, Syed M, Sexton KW, Syed S, Begum S, Syed F, Prior F, Yu F Jr. Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review. JMIR Med Inform 2021;9:e23811. [PMID: 33326405 DOI: 10.2196/23811] [Cited by in Crossref: 13] [Cited by in F6Publishing: 6] [Article Influence: 13.0] [Reference Citation Analysis]
62 Khan MA, Alhaisoni M, Tariq U, Hussain N, Majid A, Damaševičius R, Maskeliūnas R. COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion. Sensors (Basel) 2021;21:7286. [PMID: 34770595 DOI: 10.3390/s21217286] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
63 Arora V, Ng EY, Leekha RS, Darshan M, Singh A. Transfer learning-based approach for detecting COVID-19 ailment in lung CT scan. Comput Biol Med 2021;135:104575. [PMID: 34153789 DOI: 10.1016/j.compbiomed.2021.104575] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
64 Wei C. Research on university laboratory management and maintenance framework based on computer aided technology. Microprocessors and Microsystems 2020. [DOI: 10.1016/j.micpro.2020.103617] [Reference Citation Analysis]