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For: Attallah O, Ragab DA, Sharkas M. MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks. PeerJ 2020;8:e10086. [PMID: 33062453 DOI: 10.7717/peerj.10086] [Cited by in Crossref: 13] [Cited by in F6Publishing: 10] [Article Influence: 6.5] [Reference Citation Analysis]
Number Citing Articles
1 Attallah O. DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity. Diagnostics (Basel) 2021;11:2034. [PMID: 34829380 DOI: 10.3390/diagnostics11112034] [Reference Citation Analysis]
2 Bouchareb Y, Moradi Khaniabadi P, Al Kindi F, Al Dhuhli H, Shiri I, Zaidi H, Rahmim A. Artificial intelligence-driven assessment of radiological images for COVID-19. Comput Biol Med 2021;136:104665. [PMID: 34343890 DOI: 10.1016/j.compbiomed.2021.104665] [Reference Citation Analysis]
3 Naseer A, Tamoor M, Azhar A. Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs. J Xray Sci Technol 2021. [PMID: 34842222 DOI: 10.3233/XST-211047] [Reference Citation Analysis]
4 Attallah O. ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration. Comput Biol Med 2022;142:105210. [PMID: 35026574 DOI: 10.1016/j.compbiomed.2022.105210] [Reference Citation Analysis]
5 Attallah O, Anwar F, Ghanem NM, Ismail MA. Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images. PeerJ Comput Sci 2021;7:e493. [PMID: 33987459 DOI: 10.7717/peerj-cs.493] [Reference Citation Analysis]
6 Attallah O. CoMB-Deep: Composite Deep Learning-Based Pipeline for Classifying Childhood Medulloblastoma and Its Classes. Front Neuroinform 2021;15:663592. [PMID: 34122031 DOI: 10.3389/fninf.2021.663592] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
7 Ragab DA, Attallah O, Sharkas M, Ren J, Marshall S. A framework for breast cancer classification using Multi-DCNNs. Comput Biol Med 2021;131:104245. [PMID: 33556893 DOI: 10.1016/j.compbiomed.2021.104245] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
8 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]
9 Attallah O, Sharkas M. Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories. Contrast Media Mol Imaging 2021;2021:7192016. [PMID: 34621146 DOI: 10.1155/2021/7192016] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Moura LVD, Mattjie C, Dartora CM, Barros RC, Marques da Silva AM. Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography. Front Digit Health 2022;3:662343. [DOI: 10.3389/fdgth.2021.662343] [Reference Citation Analysis]
11 Attallah O, Sharkas M. GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases. PeerJ Comput Sci 2021;7:e423. [PMID: 33817058 DOI: 10.7717/peerj-cs.423] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
12 Komolafe TE, Cao Y, Nguchu BA, Monkam P, Olaniyi EO, Sun H, Zheng J, Yang X. Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis. Acad Radiol 2021;28:1507-23. [PMID: 34649779 DOI: 10.1016/j.acra.2021.08.008] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Fischer G, De Silvestro A, Müller M, Frauenfelder T, Martini K. Computer-Aided Detection of Seven Chest Pathologies on Standard Posteroanterior Chest X-Rays Compared to Radiologists Reading Dual-Energy Subtracted Radiographs. Acad Radiol 2021:S1076-6332(21)00430-X. [PMID: 34706849 DOI: 10.1016/j.acra.2021.09.016] [Reference Citation Analysis]
14 Fouladi S, Ebadi MJ, Safaei AA, Bajuri MY, Ahmadian A. Efficient deep neural networks for classification of COVID-19 based on CT images: Virtualization via software defined radio. Comput Commun 2021;176:234-48. [PMID: 34149118 DOI: 10.1016/j.comcom.2021.06.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
15 Ghimire BR, Parajuli RR, Khatiwada B, Poudel S, Sharma K, Mishra B. Covira: A COVID-19 risk assessment, visualization and communication tool. SoftwareX 2021;16:100873. [PMID: 34778507 DOI: 10.1016/j.softx.2021.100873] [Reference Citation Analysis]
16 Alshazly H, Linse C, Abdalla M, Barth E, Martinetz T. COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans. PeerJ Comput Sci 2021;7:e655. [PMID: 34401477 DOI: 10.7717/peerj-cs.655] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
17 Lu S, Zhu Z, Gorriz JM, Wang S, Zhang Y. NAGNN: Classification of COVID‐19 based on neighboring aware representation from deep graph neural network. Int J of Intelligent Sys 2022;37:1572-98. [DOI: 10.1002/int.22686] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
18 Attallah O. MB-AI-His: Histopathological Diagnosis of Pediatric Medulloblastoma and its Subtypes via AI. Diagnostics (Basel) 2021;11:359. [PMID: 33672752 DOI: 10.3390/diagnostics11020359] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 6.0] [Reference Citation Analysis]
19 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]