COVID-RDNet: A novel coronavirus pneumonia classification model using the mixed dataset by CT and X-rays images

Biocybernetics and Biomedical Engineering - Tập 42 - Trang 977-994 - 2022
Lingling Fang1, Xin Wang1
1Department of Computing and Information Technology, Liaoning Normal University, Dalian City, Liaoning Province, China

Tài liệu tham khảo

Bialek, 2020, Geographic Differences in COVID-19 Cases, Deaths, and Incidence — United States, February 12–April 7, 2020, MMWR Morb Mortal Wkly Rep, 69, 465, 10.15585/mmwr.mm6915e4 Sun, 2020, Characteristics and prognostic factors of disease severity in patients with COVID-19: The Beijing experience, J Autoimmun, 112, 10.1016/j.jaut.2020.102473 Suri J S, Puvvula A, Biswas M, et al., “COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review,” Comput Biol Med, pp. 103960, Aug. 2020. Farhat, 2020, Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19, Mach Vis Appl, 31, 1, 10.1007/s00138-020-01101-5 Sedaghat, 2020, COVID-19 protection guidelines in outpatient medical imaging centers, Acad Radiol, 27, 904, 10.1016/j.acra.2020.04.019 Li, 2020, CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19), Eur Radiol, 30, 4407, 10.1007/s00330-020-06817-6 Ko, 2020, COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image: model development and validation, J Med Intern Res, 22 Jiang, 2021, Covid-19 ct image synthesis with a conditional generative adversarial network, IEEE J Biomed Health Inf, 25, 441, 10.1109/JBHI.2020.3042523 Abd Elaziz, 2020, An improved Marine Predators algorithm with fuzzy entropy for multi-level thresholding: Real world example of COVID-19 CT image segmentation, IEEE Access, 8, 125306, 10.1109/ACCESS.2020.3007928 Apostolopoulos, 2020, Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks, Phys Eng Sci Med, 43, 635, 10.1007/s13246-020-00865-4 Abbas, 2021, Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network, Appl Intellig, 51, 854, 10.1007/s10489-020-01829-7 Hassantabar, 2020, Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches, Chaos, Solit Fract, 140, 10.1016/j.chaos.2020.110170 Civit-Masot, 2020, Deep learning system for COVID-19 diagnosis aid using X-ray pulmonary images, Appl Sci, 10, 4640, 10.3390/app10134640 Nishio, 2020, Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods, Sci Rep, 10, 1, 10.1038/s41598-020-74539-2 Ibrahim, 2022, Effective hybrid deep learning model for COVID-19 patterns identification using CT images, Exp Syst, e13010, 10.1111/exsy.13010 Shamim, 2022, Automatic COVID-19 Lung Infection Segmentation through Modified Unet Model, J Healthcare Eng, 2022, 1, 10.1155/2022/6566982 Abdulkareem, 2022, Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models, J Healthcare Eng, 2022, 1, 10.1155/2022/5329014 Krizhevsky A, Sutskever I, Hinton G E, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, pp. 1097-1105, Jan. 2012. Maghdid, 2021, “Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms”, Multimodal Image Exploitation and Learning 2021, Int Soc Optics Photon, 11734, 117340E Turkoglu, 2021, COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble, Appl Intellig, 51, 1213, 10.1007/s10489-020-01888-w Loey, 2020, M Khalifa N E, “Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning”, Symmetry, 12, 651, 10.3390/sym12040651 Sengupta, 2019, Going Deeper in Spiking Neural Networks: Vgg and Residual Architectures, Front Neurosci, 13, 10.3389/fnins.2019.00095 Sitaula, 2021, Attention-based Vgg-16 model for COVID-19 chest X-ray image classification, Applied Intelligence, 51, 2850, 10.1007/s10489-020-02055-x Shibly, 2020, COVID faster R-CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images, Inf Med Unlocked, 20 Lee, 2020, Evaluation of scalability and degree of fine-tuning of deep convolutional neural networks for COVID-19 screening on chest X-ray images using explainable deep-learning algorithm, J Personal Med, 10, 213, 10.3390/jpm10040213 He K, Zhang X, Ren S, et al., “Deep residual learning for image recognition,” Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770-778; Jun. 2016 He K, Zhang X, Ren S, et al., “Identity mappings in deep residual networks,” European conference on computer vision. Springer, Cham, pp. 630-645; Oct. 2016. Zhou, 2021, COVID-19 Detection based on Image Regrouping and Resnet-SVM using Chest X-ray Images, IEEE Access, Jan, 9, 81902, 10.1109/ACCESS.2021.3086229 Sakib, 2020, DL-CRC: deep learning-based chest radiograph classification for COVID-19 detection: a novel approach, IEEE Access, 8, 171575, 10.1109/ACCESS.2020.3025010 Hira S, Bai A, Hira S, “An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images,” Appl Intellig, vol. 51, no. 5, pp. 2864-2889, May. 2021. Huang G, Liu Z, Van Der Maaten L, et al., “Densely connected convolutional networks,” Proceedings of the IEEE conference on computer vision and pattern recognition. Pp. 4700-4708, Aug. 2017. Tabrizchi H, Mosavi A, Vamossy Z, et al., “Densely Connected Convolutional Networks (Densenet) for Diagnosing Coronavirus Disease (COVID-19) from Chest X-ray Imaging,” 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, pp. 1-5, Sept. 2021. Y.-D. Zhang S.C. Satapathy X. Zhang S.-H. Wang COVID-19 Diagnosis via DenseNet and Optimization of Transfer Learning Setting. Chowdhury, 2020, Can AI help in screening viral and COVID-19 pneumonia?, IEEE Access, 8, 132665, 10.1109/ACCESS.2020.3010287 Allioui, 2022, A multi-agent deep reinforcement learning approach for enhancement of COVID-19 CT image segmentation, J Personal Med, 12, 309, 10.3390/jpm12020309 Wang, 2021, Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network, Informat Fusion, 67, 208, 10.1016/j.inffus.2020.10.004 Tang, 2021, Severity assessment of COVID-19 using CT image features and laboratory indices, Phys Med Biol, 66, 10.1088/1361-6560/abbf9e Hasoon, 2021, COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images, Results Phys, 31, 10.1016/j.rinp.2021.105045 Tuncer, 2020, An automated residual exemplar local binary pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image, Chemomet Intellig Laborat Syst, 203 Sen, 2021, A bi-stage feature selection approach for COVID-19 prediction using chest CT images, Appl Intell, 51, 8985, 10.1007/s10489-021-02292-8 Alam, 2021, COVID-19 detection from chest X-ray images using feature fusion and deep learning, Sensors, 21, 1480, 10.3390/s21041480 Kermany, 2018, Labeled optical coherence tomography (oct) and chest x-ray images for classification, Mendeley data, 2 Bhatt, 2020, 254 Yang X, He X, Zhao J, et al. COVID-CT-Dataset: A CT Scan Dataset about COVID-19. 2020. doi: 10.48550/arXiv.2003.13865. Demir, 2020, Convolutional neural networks based efficient approach for classification of lung diseases, Health Informat Sci Syst, 8, 1 Dash, 1991, Adaptive contrast enhancement and de-enhancement, Pattern Recogn, 24, 289, 10.1016/0031-3203(91)90072-D Zhou, 2014, Global brightness and local contrast adaptive enhancement for low illumination color image, Optik, 125, 1795, 10.1016/j.ijleo.2013.09.051 Mohamed, 2018, Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation, Soft Comput, 22, 3215, 10.1007/s00500-017-2777-2 Ahmed, 2018, An enhanced adaptive P&O MPPT for fast and efficient tracking under varying environmental conditions, IEEE Trans Sustain Energy, 9, 1487, 10.1109/TSTE.2018.2791968 Meng, 2018, Parameters with adaptive learning mechanism (PALM) for the enhancement of differential evolution, Knowl-Based Syst, 141, 92, 10.1016/j.knosys.2017.11.015 Issa, 2018, ASCA-PSO: Adaptive sine cosine optimization algorithm integrated with particle swarm for pairwise local sequence alignment, Expert Syst Appl, 99, 56, 10.1016/j.eswa.2018.01.019 Shi, 2020, Normalised gamma transformation-based contrast-limited adaptive histogram equalisation with colour correction for sand–dust image enhancement, IET Image Proc, 14, 747, 10.1049/iet-ipr.2019.0992 Yu, 2017, The predictive accuracy of the black hole sign and the spot sign for hematoma expansion in patients with spontaneous intracerebral hemorrhage, Neurol Sci, 38, 1591, 10.1007/s10072-017-3006-6 Hajibandeh, 2017, Accuracy of routinely collected comorbidity data in patients undergoing colectomy: a retrospective study, Int J Colorectal Dis, 32, 1341, 10.1007/s00384-017-2830-8 Yao, 2019, Sensitivity, specificity, negative and positive predictive values of identifying atrial fibrillation using administrative data: a systematic review and meta-analysis, Clin Epidemiol, 11, 753, 10.2147/CLEP.S206267 Delate, 2017, Assessment of the coding accuracy of warfarin-related bleeding events, Thrombos Res, 159, 86, 10.1016/j.thromres.2017.10.004 Nayak, 2021, Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study, Biomed Signal Process Control, 64, 10.1016/j.bspc.2020.102365 Duffey, 2020, Analysing recovery from pandemics by Learning Theory: the case of CoVid-19, IEEE Access, 8, 110789, 10.1109/ACCESS.2020.3001344 Toğaçar M, Ergen B, Cömert Z, “COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches,” Comput Biol Med, vol. 121, p. 103805, May. 2020.