Abstract
The coronavirus (COVID-2019) quickly spread throughout the world and came to be a pandemic. To avoid further spreading this epidemic and treat affected patients rapidly, it is important to recognize the positive cases as early as possible. In this paper, deep learning techniques are employed to detect COVID-19 from chest X-ray images quickly. The images of the two classes, COVID and No-findings are collected from three public datasets. The proposed approach consists of two phases; transfer learning and fine-tuning. Transfer learning is carried out by seven deep learning models: DenseNet, Inception_Resnet, MobileNet, NASNet, ResNet, VGG, and Xception. Two fully-connected layers are added to the pre-trained model for fine-tuning. These models' performance is compared in terms of accuracy, sensitivity, specificity, and computation time. The experimental results showed that MobileNet obtained 98%, which outperformed all other accuracy and time models.
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References
Zhou, S.K., Greenspan, H., Shen, D.: Deep Learning for Medical Image Analysis. Elsevier Science (2017)
Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Acharya, U.R.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 2020 (2020)
Caobelli, F.: Artificial intelligence in medical imaging: game over for radiologists? Eur. J. Radiol. 126 (2020)
Ardakani, A.A., Kanafi, A.R., Acharya, U.R., Khadem, N., Mohammadi, A.: Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput. Biol. Med. 121 (2020)
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. 121 (2020)
El Asnaoui, K., Chawki, Y.: Using X-ray images and deep learning for automated detection of coronavirus disease. J. Biomol. Struct. Dyn., 1–12 (2020)
Wang, L., Wong, A.: COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images (2020)
Babenko, A., Slesarev, A., Chigorin, A., Lempitsky, V.: Neural codes for image retrieval, pp. 584–599. Springer, Cham (2014)
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)
Cohen, J.P.: COVID-19 Chest X-Ray Dataset or CT Dataset. GitHub (2020)
Alqudah, A.M., Qazan, S.: Augmented COVID-19 X-ray Images Dataset. In: Mendeley Data, 4 edn (2020)
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., et al.: Recent advances in convolutional neural networks. Pattern Recogn. Lett. 77, 354–377 (2018)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)
Baldi, P., Sadowski, P.J.: Understanding dropout. In: Neural Information Processing Systems 2013. Advances in Neural Information Processing Systems, pp. 2814–2822 (2013)
Wang, Y., Li, Y., Song, Y., Rong, X.: The influence of the activation function in a convolution neural network model of facial expression recognition. Appl. Sci. 10, 1897 (2020)
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T., Kendall, C.B., Gotway, M., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35, 1299–1312 (2016)
Kingma, D. P., Ba, J.: Adam: a method for stochastic optimization. CoRR, vol. abs/1412.6980 (2015)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115, 211–252 (2015)
Gardner, M.J., Altman, D.G.: Calculating confidence intervals for proportions and their differences. In: Gardner, M.J., Altman, D.G. (eds.) Statistics with confidence. BMJ Publishing Group, London, pp. 28–33 (1989)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications (2017). https://arxiv.org/abs/1704.04861
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Selim, S. (2021). Detection of COVID-19 from Chest X-Ray Images Using Deep Neural Network with Fine-Tuning Approach. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_4
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DOI: https://doi.org/10.1007/978-3-030-69717-4_4
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