We use cookies to improve your experience with our site.

Indexed in:

SCIE, EI, Scopus, INSPEC, DBLP, CSCD, etc.

Submission System
(Author / Reviewer / Editor)
Xin Zhang, Siyuan Lu, Shui-Hua Wang, Xiang Yu, Su-Jing Wang, Lun Yao, Yi Pan, Yu-Dong Zhang. Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture[J]. Journal of Computer Science and Technology, 2022, 37(2): 330-343. DOI: 10.1007/s11390-020-0679-8
Citation: Xin Zhang, Siyuan Lu, Shui-Hua Wang, Xiang Yu, Su-Jing Wang, Lun Yao, Yi Pan, Yu-Dong Zhang. Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture[J]. Journal of Computer Science and Technology, 2022, 37(2): 330-343. DOI: 10.1007/s11390-020-0679-8

Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture

Funds: This paper was supported by the Royal Society International Exchanges Cost Share Award of UK under Grant No. RP202G0230, the Medical Research Council Confidence in Concept Award of UK under Grant No. MC_PC_17171, the Hope Foundation for Cancer Research of UK under Grant No. RM60G0680, the British Heart Foundation Accelerator Award of UK under Grant No. AA/18/3/34220, Sino-UK Industrial Fund under Grant No. RP202G0289, the Global Challenges Research Fund (GCRF) of UK under Grant No. P202PF11, the Fundamental Research Funds for the Central Universities of China under Grant No. CDLS-2020-03, the Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education of China, Henan Key Research and Development Project of China, under Grant No. 182102310629, and the National Natural Science Foundation of China under Grant Nos. U19B2032 and 61772511.
More Information
  • Author Bio:

    Yu-Dong Zhang received his Ph.D. degree from Southeast University, Nanjing, in 2010. He worked as a postdoc from 2010 to 2012 with Columbia University, New York, and as an assistant research scientist from 2012 to 2013 with Research Foundation of Mental Hygiene (RFMH), New York. Now he serves as a professor with University of Leicester, Leicester, UK. Prof. Zhang was the 2019 & 2021 recipient of "Web of Science Highly Cited Researcher".

  • Corresponding author:

    Yu-Dong Zhang E-mail: yudongzhang@ieee.org

  • Received Date: June 02, 2020
  • Revised Date: January 17, 2021
  • Accepted Date: March 29, 2021
  • Published Date: March 30, 2022
  • COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

  • [1]
    Wang C, Horby P W, Hayden F G, Gao G F. A novel coronavirus outbreak of global health concern. The Lancet, 2020, 395(10223): 470-473. DOI: 10.1016/S0140-6736(20)30185-9.
    [2]
    Wang D, Hu B, Hu C et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA, 2020, 323(11): 1061-1069. DOI: 10.1001/jama.2020.1585.
    [3]
    Lu Z, Lu S Y, Liu G et al. A pathological brain detection system based on radial basis function neural network. Journal of Medical Imaging and Health Informatics, 2016, 6(5): 1218-1222. DOI: 10.1166/jmihi.2016.1901.
    [4]
    Yang J, Qiu X, Shi J P et al. A pathological brain detection system based on kernel based ELM. Multimedia Tools and Applications, 2018, 77(3): 3715-3728. DOI: 10.1007/s11042-016-3559-z.
    [5]
    Lu S, Qiu X, Shi J P et al. A pathological brain detection system based on extreme learning machine optimized by bat algorithm. CNS & Neurological Disorders-Drug Targets, 2017, 16(1): 23-29. DOI: 10.2174/1871527315666161019153259.
    [6]
    Wang S H, Li P, Chen P et al. Pathological brain detection via wavelet packet Tsallis entropy and real-coded biogeography-based optimization. Fundamenta Informaticae, 2017, 151(1/2/3/4): 275-291. DOI: 10.3233/FI-2017-1492.
    [7]
    Jiang X, Zhang Y. Chinese sign language fingerspelling recognition via six-layer convolutional neural network with leaky rectified linear units for therapy and rehabilitation. Journal of Medical Imaging and Health Informatics, 2019, 9(9): 2031-2038. DOI: 10.1166/jmihi.2019.2804.
    [8]
    Szegedy C, Liu W, Jia Y et al. Going deeper with convolutions. In Proc. the 2015 IEEE Conference on Computer Vision and Pattern Recognition, June 2015, pp.1-9. DOI: 10.1109/CVPR.2015.7298594.
    [9]
    Yu X, Wang S H. Abnormality diagnosis in mammograms by transfer learning based on ResNet18. Fundamenta Informaticae, 2019, 168(2/3/4): 219-230. DOI: 10.3233/FI-2019-1829.
    [10]
    Zhang Y D, Satapathy S C, Zhu L Y et al. A seven-layer convolutional neural network for chest CT based COVID-19 diagnosis using stochastic pooling. IEEE Sensors Journal. DOI: 10.1109/JSEN.2020.3025855.
    [11]
    Wu S, Wu X, Zhang Yet al. Diagnosis of COVID-19 by wavelet renyi entropy and three-segment biogeography-based optimization. International Journal of Computational Intelligence Systems, 2020, 13(1): 1332-1344. DOI: 10.2991/ijcis.d.200828.001.
    [12]
    Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238. DOI: 10.1109/TPAMI.2005.159.
    [13]
    Chung M, Bernheim A, Mei X et al. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology, 2020, 295(1): 202-207. DOI: 10.1148/radiol.2020200230.
    [14]
    Maghdid H S, Ghafoor K Z, Sadiq A S et al. A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: Design study. arXiv:2003.07434, 2020. https://arxiv.org/abs/2003.07434, Dec. 2020.
    [15]
    Wang L, Wong A. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv:2003.09871, 2020. https://arxiv.org/abs/2003.09871, Dec. 2020.
    [16]
    Al-Karawi D, Al-Zaidi S, Polus N, Jassim S. Machine learning analysis of chest CT scan images as a complementary digital test of coronavirus (COVID-19) patients. medRxiv. DOI: 10.1101/2020.04.13.20063479.
    [17]
    Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In Proc. the 25th International Conference on Neural Information Processing Systems, December 2012, pp.1097-1105. DOI: 10.1145/3065386.
    [18]
    Shakarami A, Tarrah H, Mahdavi-Hormat A. A CAD system for diagnosing Alzheimer's disease using 2D slices and an improved AlexNet-SVM method. Optik, 2020, 212: Article No. 164237. DOI: 10.1016/j.ijleo.2020.164237.
    [19]
    Wang R, Xu J, Han T X. Object instance detection with pruned Alexnet and extended training data. Signal Processing: Image Communication, 2019, 70: 145-156. DOI: 10.1016/j.image.2018.09.013.
    [20]
    Szymak P, Gasiorowski M. Using pretrained AlexNet deep learning neural network for recognition of underwater objects. Na\v{s}e More, 2020, 67(1): 9-13. DOI: 10.17818/NM/2020/1.2.
    [21]
    Guo C J, Xu Y L, Tian Z. Inversion of PM2.5 atmospheric refractivity profile based on AlexNet model from the perspective of electromagnetic wave propagation. Environmental Science and Pollution Research, 2020, 27(30): 37333-37346. DOI: 10.1007/s11356-020-07703-w.
    [22]
    Zhao X Y, Dong C Y, Zhou P, Zhu M J, Ren J W, Chen X Y. Detecting surface defects of wind tubine blades using an Alexnet deep learning algorithm. IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2019, E102A(12): 1817-1824. DOI: 10.1587/transfun.E102.A.1817.
    [23]
    Xiao L, Yan Q, Deng S. Scene classification with improved AlexNet model. In Proc. the 12th International Conference on Intelligent Systems and Knowledge Engineering, Nov. 2017. DOI: 10.1109/ISKE.2017.8258820.
    [24]
    Rakitianskaia A, Engelbrecht A. Measuring saturation in neural networks. In Proc. the 2015 IEEE Symposium Series on Computational Intelligence, Dec. 2015, pp.1423-1430. DOI: 10.1109/SSCI.2015.202.
    [25]
    Gertych A, Swiderska-Chadaj Z, Ma Z et al. Convolutional neural networks can accurately distinguish four histologic growth patterns of lung adenocarcinoma in digital slides. Sci. Rep., 2019, 9(1): Article No. 1483. DOI: 10.1038/s41598-018-37638-9.
    [26]
    Fukae J, Isobe M, Hattori T et al. Convolutional neural network for classification of two-dimensional array images generated from clinical information may support diagnosis of rheumatoid arthritis. Sci. Rep., 2020, 10(1): Article No. 5648. DOI: 10.1038/s41598-020-62634-3.
    [27]
    Nguyen H D, Lloyd-Jones L R, McLachlan G J. A universal approximation theorem for mixture-of-experts models. Neural Computation, 2016, 28(12): 2585-2593. DOI: 10.1162/NECO_a_00892.
    [28]
    Huang Y, Yang D, Wang K, Wang L, Fan J. A quality diagnosis method of GMAW based on improved empirical mode decomposition and extreme learning machine. Journal of Manufacturing Processes, 2020, 54: 120-128. DOI: 10.1016/j.jmapro.2020.03.006.
    [29]
    Schmidt W F, Kraaijveld M A, Duin R P W. Feedforward neural networks with random weights. In Proc. the 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, Aug. 30-Sept. 3, 1992. DOI: 10.1109/ICPR.1992.201708.
    [30]
    Pao Y H, Park G H, Sobajic D J. Learning and generalization characteristics of the random vector functional-link net. Neurocomputing, 1994, 6(2): 163-180. DOI: 10.1016/0925-2312(94)90053-1.
    [31]
    Kushwah G S, Ranga V. Voting extreme learning machine based distributed denial of service attack detection in cloud computing. Journal of Information Security and Applications, 2020, 53: Article No. 102532. DOI: 10.1016/j.jisa.2020.102532.
    [32]
    Yager R R, Kreinovich V. Universal approximation theorem for uninorm-based fuzzy systems modeling. Fuzzy Sets and Systems, 2003, 140(2): 331-339. DOI: 10.1016/S0165-0114(02)00521-3.
    [33]
    Scardapane S, Fierimonte R, Wang D H, Panella M, Uncini A. Distributed music classification using random vector functional-link nets. In Proc. the 2015 International Joint Conference on Neural Networks, July 2015. DOI: 10.1109/IJCNN.2015.7280333.
    [34]
    Chaudhuri A. The minimization of empirical risk through stochastic gradient descent with momentum algorithms. In Proc. the 8th Computer Science On-line Conference on Artificial Intelligence Methods in Intelligent Algorithms, April 2019, pp.168-181. DOI: 10.1007/978-3-030-19810-7_17.
    [35]
    Dean J, Corrado G, Monga R et al. Large scale distributed deep networks. In Proc. the 25th International Conference on Neural Information Processing Systems, December 2012, pp.1223-1231.
    [36]
    Rajaraman S, Candemir S, Kim I, Thoma G, Antani S. Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Applied Sciences, 2018, 8(10): Article No. 1715. DOI: 10.3390/app8101715.
    [37]
    Ardila D, Kiraly A P, Bharadwaj S et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 2019, 25(6): 954-961. DOI: 10.1038/s41591-019-0447-x.
    [38]
    Chae K J, Jin G Y, Ko S B, Wang Y, Zhang H, Choi E J, Choi H. Deep learning for the classification of small (≤2 cm) pulmonary nodules on CT imaging: A preliminary study. Acad. Radiol., 2020, 27(4): e55-e63. DOI: 10.1016/j.acra.2019.05.018.
    [39]
    Koo H J, Lim S, Choe J, Choi S H, Sung H, Do K H. Radiographic and CT features of viral pneumonia. RadioGraphics, 2018, 38(3): 719-739. DOI: 10.1148/rg.2018170048.
  • Related Articles

    [1]Lei Guan, Dong-Sheng Li, Ji-Ye Liang, Wen-Jian Wang, Ke-Shi Ge, Xi-Cheng Lu. Advances of Pipeline Model Parallelism for Deep Learning Training: An Overview[J]. Journal of Computer Science and Technology, 2024, 39(3): 567-584. DOI: 10.1007/s11390-024-3872-3
    [2]Adam Weingram, Yuke Li, Hao Qi, Darren Ng, Liuyao Dai, Xiaoyi Lu. xCCL: A Survey of Industry-Led Collective Communication Libraries for Deep Learning[J]. Journal of Computer Science and Technology, 2023, 38(1): 166-195. DOI: 10.1007/s11390-023-2894-6
    [3]Sheng-Luan Hou, Xi-Kun Huang, Chao-Qun Fei, Shu-Han Zhang, Yang-Yang Li, Qi-Lin Sun, Chuan-Qing Wang. A Survey of Text Summarization Approaches Based on Deep Learning[J]. Journal of Computer Science and Technology, 2021, 36(3): 633-663. DOI: 10.1007/s11390-020-0207-x
    [4]Hua Chen, Juan Liu, Qing-Man Wen, Zhi-Qun Zuo, Jia-Sheng Liu, Jing Feng, Bao-Chuan Pang, Di Xiao. CytoBrain: Cervical Cancer Screening System Based on Deep Learning Technology[J]. Journal of Computer Science and Technology, 2021, 36(2): 347-360. DOI: 10.1007/s11390-021-0849-3
    [5]Jun Gao, Paul Liu, Guang-Di Liu, Le Zhang. Robust Needle Localization and Enhancement Algorithm for Ultrasound by Deep Learning and Beam Steering Methods[J]. Journal of Computer Science and Technology, 2021, 36(2): 334-346. DOI: 10.1007/s11390-021-0861-7
    [6]Wei Du, Yu Sun, Hui-Min Bao, Liang Chen, Ying Li, Yan-Chun Liang. DeepHBSP: A Deep Learning Framework for Predicting Human Blood-Secretory Proteins Using Transfer Learning[J]. Journal of Computer Science and Technology, 2021, 36(2): 234-247. DOI: 10.1007/s11390-021-0851-9
    [7]Andrea Caroppo, Alessandro Leone, Pietro Siciliano. Comparison Between Deep Learning Models and Traditional Machine Learning Approaches for Facial Expression Recognition in Ageing Adults[J]. Journal of Computer Science and Technology, 2020, 35(5): 1127-1146. DOI: 10.1007/s11390-020-9665-4
    [8]Nuo Qun, Hang Yan, Xi-Peng Qiu, Xuan-Jing Huang. Chinese Word Segmentation via BiLSTM+Semi-CRF with Relay Node[J]. Journal of Computer Science and Technology, 2020, 35(5): 1115-1126. DOI: 10.1007/s11390-020-9576-4
    [9]Hui-Ying Lan, Lin-Yang Wu, Xiao Zhang, Jin-Hua Tao, Xun-Yu Chen, Bing-Rui Wang, Yu-Qing Wang, Qi Guo, Yun-Ji Chen. DLPlib: A Library for Deep Learning Processor[J]. Journal of Computer Science and Technology, 2017, 32(2): 286-296. DOI: 10.1007/s11390-017-1722-2
    [10]Ma Zhifang. DKBLM——Deep Knowledge Based Learning Methodology[J]. Journal of Computer Science and Technology, 1993, 8(4): 93-98.
  • Others

  • Cited by

    Periodical cited type(45)

    1. Jerald Prasath G, Prabu S, V. Valli Mayil, et al. Optimized double transformer residual super-resolution network-based X-ray images for classification of pneumonia identification. Knowledge-Based Systems, 2025, 311: 113037. DOI:10.1016/j.knosys.2025.113037
    2. K. Balasamy, V. Seethalakshmi. HCO-RLF: Hybrid classification optimization using recurrent learning and fuzzy for COVID-19 detection on CT images. Biomedical Signal Processing and Control, 2025, 100: 106951. DOI:10.1016/j.bspc.2024.106951
    3. Ali Khalili Fakhrabadi, Mehdi Jafari Shahbazzadeh, Nazanin Jalali, et al. A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity. Scientific Reports, 2025, 15(1) DOI:10.1038/s41598-025-91322-3
    4. Burhan Ul Haque Sheikh, Aasim Zafar. White-box inference attack: compromising the security of deep learning-based COVID-19 diagnosis systems. International Journal of Information Technology, 2024, 16(3): 1475. DOI:10.1007/s41870-023-01538-7
    5. S. Maheswari, S. Suresh, S. Ahamed Ali. A systematic literature review on machine learning and deep learning-based covid-19 detection frameworks using X-ray Images. Applied Soft Computing, 2024, 166: 112137. DOI:10.1016/j.asoc.2024.112137
    6. Goizalde Badiola-Zabala, Jose Manuel Lopez-Guede, Julian Estevez, et al. Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022. Electronics, 2024, 13(6): 1005. DOI:10.3390/electronics13061005
    7. Cheng-Tang Pan, Rahul Kumar, Zhi-Hong Wen, et al. Improving Respiratory Infection Diagnosis with Deep Learning and Combinatorial Fusion: A Two-Stage Approach Using Chest X-ray Imaging. Diagnostics, 2024, 14(5): 500. DOI:10.3390/diagnostics14050500
    8. G. Sripriyanka, Anand Mahendran. Securing IoMT: A Hybrid Model for DDoS Attack Detection and COVID-19 Classification. IEEE Access, 2024, 12: 17328. DOI:10.1109/ACCESS.2024.3354034
    9. Fanfeng Shi, Jiaji Wang, Vishnuvarthanan Govindaraj. SGS: SqueezeNet-guided Gaussian-kernel SVM for COVID-19 Diagnosis. Mobile Networks and Applications, 2024. DOI:10.1007/s11036-023-02288-3
    10. Sheena Christabel Pravin, G. Rohith, Kiruthika V, et al. PixNet for early diagnosis of COVID-19 using CT images. Multimedia Tools and Applications, 2024. DOI:10.1007/s11042-024-19221-9
    11. Shui-Hua Wang, Suresh Chandra Satapathy, Man-Xia Xie, et al. RETRACTED ARTICLE: ELUCNN for explainable COVID-19 diagnosis. Soft Computing, 2024, 28(S2): 455. DOI:10.1007/s00500-023-07813-w
    12. Junwen Chen, Tong Liu, Yangguang Cui, et al. A meta-learning based method for few-shot pneumonia identification using chest X-ray images. Biomedical Signal Processing and Control, 2024, 95: 106433. DOI:10.1016/j.bspc.2024.106433
    13. G Divya Deepak. Optimization of deep neural network for multiclassification of Pneumonia. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2024, 12(1) DOI:10.1080/21681163.2023.2292072
    14. Ali Akbar Siddique, Wadii Boulila, Mohammed S. Alshehri, et al. Privacy-Enhanced Pneumonia Diagnosis: IoT-Enabled Federated Multi-Party Computation in Industry 5.0. IEEE Transactions on Consumer Electronics, 2024, 70(1): 1923. DOI:10.1109/TCE.2023.3319565
    15. Muhammad Umair Ali, Amad Zafar, Jawad Tanveer, et al. Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection. International Journal of Imaging Systems and Technology, 2024, 34(2) DOI:10.1002/ima.23001
    16. Burhan Ul Haque Sheikh, Aasim Zafar. Removing Adversarial Noise in X-ray Images via Total Variation Minimization and Patch-Based Regularization for Robust Deep Learning-based Diagnosis. Journal of Imaging Informatics in Medicine, 2024, 37(6): 3282. DOI:10.1007/s10278-023-00919-5
    17. Yu-Dong Zhang, Yanrong Pei, Juan Manuel Górriz. SCNN: A Explainable Swish-based CNN and Mobile App for COVID-19 Diagnosis. Mobile Networks and Applications, 2023, 28(5): 1936. DOI:10.1007/s11036-023-02161-3
    18. WEI WANG, YANRONG PEI, SHUI-HUA WANG, et al. PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN. BIOCELL, 2023, 47(2): 373. DOI:10.32604/biocell.2023.025905
    19. Omneya Attallah. RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics. Chemometrics and Intelligent Laboratory Systems, 2023, 233: 104750. DOI:10.1016/j.chemolab.2022.104750
    20. Mana Saleh Al Reshan, Kanwarpartap Singh Gill, Vatsala Anand, et al. Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model. Healthcare, 2023, 11(11): 1561. DOI:10.3390/healthcare11111561
    21. Wanchun Sun, Xin Feng, Jingyao Liu, et al. Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images. Biomedical Signal Processing and Control, 2023, 79: 104099. DOI:10.1016/j.bspc.2022.104099
    22. Shiyue Huang, Ziwei Wang, Xinyi Zhang, et al. DBPA: A Benchmark for Transactional Database Performance Anomalies. Proceedings of the ACM on Management of Data, 2023, 1(1): 1. DOI:10.1145/3588926
    23. Varshini S, Ramprasad R, Sivakumar M. Pneumonia Detection Using Image Enhancing Techniques and Deep Learning. international journal of engineering technology and management sciences, 2023, 7(2): 762. DOI:10.46647/ijetms.2023.v07i02.082
    24. Xingze Wang, Guoxian Yu, Zhongmin Yan, et al. Lung Cancer Subtype Diagnosis by Fusing Image-Genomics Data and Hybrid Deep Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023, 20(1): 512. DOI:10.1109/TCBB.2021.3132292
    25. Haoyang Zhou, Haojiang Li, Shuchao Chen, et al. BSMM-Net: Multi-modal neural network based on bilateral symmetry for nasopharyngeal carcinoma segmentation. Frontiers in Human Neuroscience, 2023, 16 DOI:10.3389/fnhum.2022.1068713
    26. Jingzhang Sun, Bang-Hung Yang, Chien-Ying Li, et al. Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network. Frontiers in Medicine, 2023, 10 DOI:10.3389/fmed.2023.1083413
    27. Bedanta Bhattacharjee, Abu Md Ashif Ikbal, Atika Farooqui, et al. Superior possibilities and upcoming horizons for nanoscience in COVID-19: noteworthy approach for effective diagnostics and management of SARS-CoV-2 outbreak. Chemical Papers, 2023, 77(8): 4107. DOI:10.1007/s11696-023-02795-3
    28. Hai-yan Yao, Wang-gen Wan, Xiang Li. A deep adversarial model for segmentation-assisted COVID-19 diagnosis using CT images. EURASIP Journal on Advances in Signal Processing, 2022, 2022(1) DOI:10.1186/s13634-022-00842-x
    29. Jiawei Mao, Xuesong Yin, Guodao Zhang, et al. Pseudo-labeling generative adversarial networks for medical image classification. Computers in Biology and Medicine, 2022, 147: 105729. DOI:10.1016/j.compbiomed.2022.105729
    30. Xue Han, Zuojin Hu, Shuihua Wang, et al. A Survey on Deep Learning in COVID-19 Diagnosis. Journal of Imaging, 2022, 9(1): 1. DOI:10.3390/jimaging9010001
    31. Yanyan Mao, Chao Chen, Zhenjie Wang, et al. Generative adversarial networks with adaptive normalization for synthesizing T2-weighted magnetic resonance images from diffusion-weighted images. Frontiers in Neuroscience, 2022, 16 DOI:10.3389/fnins.2022.1058487
    32. Yi-Zhong Wang, David G. Birch. Performance of Deep Learning Models in Automatic Measurement of Ellipsoid Zone Area on Baseline Optical Coherence Tomography (OCT) Images From the Rate of Progression of USH2A-Related Retinal Degeneration (RUSH2A) Study. Frontiers in Medicine, 2022, 9 DOI:10.3389/fmed.2022.932498
    33. Jiaji Wang. A Review of Deep Learning-Based Methods for the Diagnosis and Prediction of COVID-19. International Journal of Patient-Centered Healthcare, 2022, 12(1): 1. DOI:10.4018/IJPCH.311444
    34. Yeong-Hun Song, Jun-Young Yi, Young Noh, et al. On the reliability of deep learning-based classification for Alzheimer’s disease: Multi-cohorts, multi-vendors, multi-protocols, and head-to-head validation. Frontiers in Neuroscience, 2022, 16 DOI:10.3389/fnins.2022.851871
    35. Xixiang Lin, Feifei Yang, Yixin Chen, et al. Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction. Frontiers in Cardiovascular Medicine, 2022, 9 DOI:10.3389/fcvm.2022.903660
    36. Zhe Li, Dehua Hu. Forecast of the COVID-19 Epidemic Based on RF-BOA-LightGBM. Healthcare, 2021, 9(9): 1172. DOI:10.3390/healthcare9091172
    37. Yunqing Liu, Yanrui Jin, Jinlei Liu, et al. Precise and efficient heartbeat classification using a novel lightweight-modified method. Biomedical Signal Processing and Control, 2021, 68: 102771. DOI:10.1016/j.bspc.2021.102771
    38. Payman Hussein Hussan, Israa Hadi Ali. A comprehensive survey on Covid-19 disease diagnosis: Datasets, deep learning approaches and challenges. TRANSPORT, ECOLOGY, SUSTAINABLE DEVELOPMENT: EKO VARNA 2023, DOI:10.1063/5.0191721
    39. Jia-Ji Wang, Yangrong Pei, Liam O’Donnell, et al. Multimedia Technology and Enhanced Learning. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, DOI:10.1007/978-3-031-18123-8_50
    40. Xiaoyu Tang, HuiLong Chen, Hui Ye, et al. 7th International Conference on Computing, Control and Industrial Engineering (CCIE 2023). Lecture Notes in Electrical Engineering, DOI:10.1007/978-981-99-2730-2_39
    41. Guangling Qi, Linna Zhao, Yuanhang Di. Multi-view Information Fusion Network for Pneumonia Diagnosis from Full Sequence CTs. 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), DOI:10.1109/ICIBA56860.2023.10164992
    42. Jia-Ji Wang. Intelligent Computing Theories and Application. Lecture Notes in Computer Science, DOI:10.1007/978-3-031-13829-4_52
    43. Shagun Sharma, Kalpna Guleria. Pneumonia Detection from Chest X-ray Images using Transfer Learning. 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), DOI:10.1109/ICRITO56286.2022.9964588
    44. Jiaji Wang, Shuwen Chen, Huisheng Zhu. Computational and Experimental Simulations in Engineering. Mechanisms and Machine Science, DOI:10.1007/978-3-031-44947-5_20
    45. Md Rabiul Hasan, Shah Muhammad Azmat Ullah, Mehedi Hasan. Deep Learning in Radiology:A Transfer-Learning Based Approach for the Identification and Classification of COVID-19 and Pneumonia in Chest X-ray Images. 2023 Fourth International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), DOI:10.1109/ICSTCEE60504.2023.10585226

    Other cited types(0)

Catalog

    Article views (101) PDF downloads (0) Cited by(45)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return