Abstract
COVID-19 caused a pandemic outbreak, resulting in many deaths and severe economic damage since 2019. Hence, the diagnosis of COVID-19 has become one of the major fields of research. Although RT-PCR has excellent reliability and precision, it is time-consuming and laborious. Therefore, the chest X-ray was used as an alternative and reliable diagnostic tool for COVID-19. However, it requires a radiologist to analyze the X-ray images, which is limited by the availability of experts and time. Henceforth, many researchers deployed automated computer-aided diagnosis with deep learning neural networks to speed up the diagnosis of COVID-19 with high accuracy and reproducibility. This study applied six state-of-art convolutional neural networks (DenseNet201, MobileNetV2, ResNet101V2, VGG16, InceptionNetV3, and Xception) with transfer learning. An integrated stacking ensemble method was used to concatenate DenseNet201, MobileNetV2, VGG16, and Xception to produce a robust and accurate diagnostic model for COVID-19. The proposed ensembled CNN model in this study produced a test accuracy of 0.9725, sensitivity of 0.9749, and F1-score of 0.9724.
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References
Feehan J, Apostolopoulos V (2021) Is COVID-19 the worst pandemic? Maturitas 2021(149):56–58
Lotfi M, Hamblin MR, Rezaei N (2020) COVID-19: transmission, prevention, and potential therapeutic opportunities. Clin Chim Acta Int J Clin Chem 2020:508
Worldometer. COVID—coronavirus statistics. https://www.worldometers.info/coronavirus/. Accessed 21 Apr 2023
Mardani R, Ahmadi Vasmehjani A, Zali F, Gholami A, Mousavi Nasab SD, Kaghazian H et al (2020) Laboratory parameters in detection of COVID-19 patients with positive RT-PCR; a diagnostic accuracy study. Arch Acad Emerg Med 8(1):e43
Keidar D, Yaron D, Goldstein E, Shachar Y, Blass A, Charbinsky L et al (2021) COVID-19 classification of X-ray images using deep neural networks. Eur Radiol 31(12):9654–9663
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Rajendra Acharya U (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792
Khan AI, Shah JL, Bhat MM (2020) CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Prog Biomed 196:105581
Punn NS, Agarwal S (2021) Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks. Appl Intell 51(5):2689–2702
Tang GS, Chow LS, Solihin MI, Ramli N, Gowdh NF, Rahmat K (2021) Detection of COVID-19 using deep convolutional neural network on chest X-ray (CXR) images. In: 2021 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp 1–6. IEEE
Chow LS, Tang GS, Solihin MI, Gowdh NM, Ramli N, Rahmat K (2023) Quantitative and qualitative analysis of 18 deep convolutional neural network (CNN) models with transfer learning to diagnose COVID-19 on chest X-ray (CXR) images. SN Comput Sci 4(2):141
QaTa-COV19 Dataset. https://www.kaggle.com/datasets/aysendegerli/qatacov19-dataset. Accessed 20 Apr 2023
Agarap AF (2019) Deep learning using rectified linear units (ReLU). arXiv February 7, 2019
Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. arXiv January 29, 2017. http://arxiv.org/abs/1412.6980. Accessed 30 Mar 2023
Bottou L (2012) Stochastic gradient descent tricks. In: Montavon G, Orr GB, Müller K-R (eds) Neural networks: tricks of the trade, 2nd Edn. Lecture Notes in Computer Science. Springer, Berlin, pp 421–436
Saha P, Sadi MS, Islam MdM (2021) EMCNet: automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers. Inform Med 22:100505
Afifi A, Hafsa NE, Ali MAS, Alhumam A, Alsalman S (2021) An ensemble of global and local-attention based convolutional neural networks for COVID-19 diagnosis on chest X-ray images. Symmetry 113:13
Ismail AHM, Abdullah MA, Khairuddin IM, Mohd Isa WH, Razman MAM, Jizat JAM, Abdul Majeed PP (2021) The Diagnosis of COVID-19 Through X-Ray Images via Transfer Learning Pipeline. In: Mat Jizat JA, Khairuddin IM, Mohd Razman MA, Ab Nasir AF, Abdul Karim MS, Jaafar AA, Hong LW, Abdul Majeed AP, Liu P, Myung H, Choi H-L, Susto G-A (eds) Advances in robotics, automation and data analytics, Advances in intelligent systems and computing. Springer International Publishing, Cham, pp 378–384
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2261–2269. IEEE, Honolulu, HI
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This study is funded by the UCSI University, Research Excellence and Innovation Grant, grant number REIG-FETBE-2022/024.
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Low, W.S., Chow, L.S., Solihin, M.I., Handayani, D.O.D. (2024). Diagnosis of COVID-19 on Chest X-ray (CXR) Images Using CNN with Transfer Learning and Integrated Stacking Ensemble Learning. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_1
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DOI: https://doi.org/10.1007/978-981-99-8819-8_1
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