Efficient deep learning approach for augmented detection of Coronavirus disease

Neural Computing and Applications - Tập 34 Số 14 - Trang 11423-11440 - 2022
Ahmed Sedik1, Mohamed Hammad2, Fathi E. Abd El‐Samie3, Brij B. Gupta4, Ahmed A. Abd El‐Latif5
1Department of the Robotics and Intelligent Machines, Kafrelsheikh University, Kafrelsheikh, Egypt
2Information Technology Department, Faculty of Computers and Information, Menoufia University, Shebeen El-Kom, Egypt
3Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf 32952, Egypt
4National Institute of Technology, Kurukshetra, India
5Department of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebeen El-Kom, 32511, Egypt

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World Health Coronavirus Disease (COVID-2019) (2020) Situation reports, [Online]. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. Accessed 15 Oct 2020

Dorgham O, Al-Rahamneh B, Almomani A, Khatatneh KF (2018) Enhancing the security of exchanging and storing DICOM medical images on the cloud. Int J Cloud Appl Comput IJCAC 8(1):154–172

Gudivada A, Philips J, Tabrizi N (2020) Developing concept enriched models for big data processing within the medical domain. Int J Softw Sci Comput Intell IJSSCI 12(3):55–71

Qureshi B (2018) An affordable hybrid cloud based cluster for secure health informatics research. Int J Cloud Appl Comput IJCAC 8(2):27–46

Sarivougioukas J, Vagelatos A (2020) Modeling deep learning neural networks with denotational mathematics in UbiHealth environment. Int J Softw Sci Comput Intell IJSSCI 12(3):14–27

Goléa NEH, Melkemi KE (2019) ROI-based fragile watermarking for medical image tamper detection. Int J High Perform Comput Netw 13(2):199–210

Ghoneim A, Muhammad G, Amin SU, Gupta B (2018) Medical image forgery detection for smart healthcare. IEEE Commun Mag 56(4):33–37

Bai X et al (2020) Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology. https://doi.org/10.1148/radiol.2020200823

Raoofi A, Takian A, Sari AA, Olyaeemanesh A, Haghighi H, Aarabi M (2020) COVID-19 pandemic and comparative health policy learning in Iran. Arch Iran Med 23(4):220–234

Rajaraman S, Antani SK (2020) Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection. medRxiv

Asif S, Wenhui Y (2020) Automatic detection of COVID-19 using X-ray images with deep convolutional neural networks and machine learning. medRxiv

de Moura J, Novo J, Ortega M (2020) Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images. medRxiv

Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2020) Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 32(3):839–854

Razzak MI, Imran M, Xu G (2020) Big data analytics for preventive medicine. Neural Comput Appl 32(9):4417–4451

Li D, Deng L et al (2019) A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Inf Sci 479:432–447

Abbas A, Abdelsamea MM, Gaber MM (2020) Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. arXiv preprint arXiv:2003.13815

Farooq M, Hafeez A (2020) COVID-resnet: a deep learning framework for screening of COVID-19 from radiographs. arXiv preprint arXiv:2003.14395

Hall LO, Paul R, Goldgof DB, Goldgof GM (2020) Finding COVID-19 from chest x-rays using deep learning on a small dataset. arXiv preprint arXiv:2004.02060

Gozes O, Frid-Adar M, Sagie N, Zhang H, Ji W, Greenspan H (2020) Coronavirus detection and analysis on chest ct with deep learning. arXiv preprint arXiv:2004.02640

AlZu’bi S, Shehab M, Al-Ayyoub M, Jararweh Y, et al (2020) Parallel implementation for 3d medical volume fuzzy segmentation. Pattern Recogn Lett 130:312–318

Wang H, Li Z, Li Y, Gupta BB, Choi C (2020) Visual saliency guided complex image retrieval. Pattern Recogn Lett 130:64–72

He X, Yang X, Zhang S, Zhao J, Zhang Y, Xing E, Xie P (2020) Sample-efficient deep learning for COVID-19 diagnosis based on CT scans. medRxiv

Jelodar H, Wang Y, Orji R, Huang H (2020) Deep Sentiment classification and topic discovery on novel coronavirus or COVID-19 online discussions: NLP using LSTM recurrent neural network approach. arXiv preprint arXiv:2004.11695

Yan B, Tang X, Liu B, Wang J, Zhou Y, Zheng G, Zou Q, Lu Y, Tu W (2020) An improved method of COVID-19 case fitting and prediction based on LSTM. arXiv preprint arXiv:2005.03446

Wang L, Li L, Li J, Li J, Gupta BB, Liu X (2018) Compressive sensing of medical images with confidentially homomorphic aggregations. IEEE Internet Things J 6(2):1402–1409

Sedik A, Iliyasu AM, El-Rahiem A, Abdel Samea ME, Abdel-Raheem A, Hammad M, Peng J, El-Samie A, Fathi E, El-Latif AAA (2020) Deploying machine and deep learning models for efficient data-augmented detection of COVID-19 infections. Viruses 12(7):769

Zhang Z (2018) Improved adam optimizer for deep neural networks. In: 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS), pp 1–2. IEEE

El-Ashkar AM, Sedik A, Shendy H, Taha TES, El-Fishawy AS, El-Nabi A, Khalaf AA, El-Banby GH, El-Samie A, Fathi E (2019) Classification of reconstructed SAR images based on convolutional neural network. Menoufia J Electron Eng Res 28(ICEEM2019-Special Issue):122–125

Al-Azrak FM, Sedik A, Dessowky MI, El Banby GM, Khalaf AA, Elkorany AS, El-Samie FEA (2020) An efficient method for image forgery detection based on trigonometric transforms and deep learning. Multimed Tools Appl 79:1–23

Abd El-Rahiem B, Sedik A, El Banby GM, Ibrahem HM, Amin M, Song OY, Khalaf AAM, Abd El-Samie FE (2020) An efficient deep learning model for classification of thermal face images. J Enterp Inf Manag

COVID-CT-Dataset (2020) A CT scan dataset about COVID-19. https://github.com/UCSD-AI4H/COVID-CT. Accessed 15 April 2020

Alqudah AM, Qazan S (2020) Augmented COVID-19 x-ray images dataset, vol 4. https://doi.org/10.17632/2FXZ4PX6D8.4

https://www.kaggle.com/tawsifurrahman/covid19-radiography-database. Accessed 15 Oct 2020

Sedik A, El-Samie A, Fathi E, El-Latif A, Ahmed A, Hammad M (2020) Combined COVID-19 dataset. Mendeley Data. https://doi.org/10.17632/3pxjb8knp7.3

Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Liu W, Wang X (2020) Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv

Ghoshal B, Tucker A (2020) Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv preprint arXiv:2003.10769

Alom MZ, Rahman MM, Nasrin MS, Taha TM, Asari VK (2020) COVID_MTNet: COVID-19 detection with multi-task deep learning approaches. arXiv preprint arXiv:2004.03747

Amrani M, Hammad M, Jiang F, Wang K, Amrani A (2018) Very deep feature extraction and fusion for arrhythmias detection. Neural Comput Appl 30(7):2047–2057

Alghamdi A, Hammad M, Ugail H, Abdel-Raheem A, Muhammad K, Khalifa HS, Abd El-Latif AA (2020) Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities. Multimed Tools Appl 1–22

Khatami A, Nazari A, Khosravi A, Lim CP, Nahavandi S (2020) A weight perturbation-based regularisation technique for convolutional neural networks and the application in medical imaging. Expert Syst Appl 149:113196

Hajabdollahi M, Esfandiarpoor R, Sabeti E, Karimi N, Soroushmehr SMR, Samavi S (2020) Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network. Biomed Sig Process Control 57:101792