[1] A.M. Alqudah, S. Qazan, and A. Alqudah, Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks, preprint, In Review, April 2020.
[2] I.D. Apostolopoulos and T.A. Mpesiana, Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks, Phys. Engin. Sci. Medic. 43 (2020), no. 2, 635–640.
[3] P.R.A.S. Bassi and R. Attux, A deep convolutional neural network for COVID-19 detection using chest X-rays, Res. Biomed. Engin. 38 (2022), no. 1, 139–148.
[4] L. Cai, J. Gao, and D. Zhao, A review of the application of deep learning in medical image classification and segmentation, Ann. Translat. Medic. 8 (2020), no. 11, 713–713.
[5] L. Falzone, G. Gattuso, A. Tsatsakis, D. Spandidos, and M. Libra, Current and innovative methods for the diagnosis of COVID-19 infection (Review), Int. J. Molecul. Medic. 47 (2021), no. 6, 100.
[6] E. Hussain, M. Hasan, M.A. Rahman, I. Lee, T. Tamanna, and M.Z. Parvez, CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images, Chaos Solitons Fractals 142 (2021), 110495.
[7] E. Irmak, A novel deep convolutional neural network model for COVID-19 disease detection, 2020 Medical Technologies Congress (TIPTEKNO) (Antalya, Turkey), IEEE, November 2020, pp. 1–4.
[8] B. Jabber, J. Lingampalli, C.Z.n Basha, and A. Krishna, Detection of Covid-19 patients using chest X-ray images with convolution neural network and mobile net, 3rd Int. Conf. Intel Sustain Syst (ICISS) (Thoothukudi, India), IEEE, December 2020, pp. 1032–1035.
[9] Kaggle Team, Kaggle, 2022.
[10] E.B.G. Kana, M.G.Z. Kana, A.F.D. Kana, and R.H.A. Kenfack, A web-based diagnostic tool for COVID-19 using machine learning on chest radiographs (CXR), preprint, Health Informatics, April 2020.
[11] Z. Karhan and F. Akal, Covid-19 classification using deep learning in chest X-ray images, 2020 Medical Technologies Congress (TIPTEKNO) (Antalya, Turkey), IEEE, November 2020, pp. 1–4.
[12] S.H. Khan, A. Sohail, M.M. Zafar, and A. Khan, Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network, Photodiagn. Photodyn. Therapy 35 (2021), 102473.
[13] H. Li, J. Li, X. Guan, B. Liang, Y. Lai, and X. Luo, Research on overfitting of deep learning, 15th Int. Conf. Comput. Intel. Secur. (CIS) (Macao, Macao), IEEE, December 2019, pp. 78–81.
[14] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, A survey of convolutional neural networks: Analysis, applications, and prospects, IEEE Trans. Neural Networks Learn. Syst. 33 (2022), no. 12, 6999–7019.
[15] A. Narin, C. Kaya, and Z. Pamuk, Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks, Pattern Anal. Appl. 24 (2021), no. 3, 1207–1220.
[16] Tulin Ozturk, Muhammed Talo, Eylul Azra Yildirim, Ulas Baran Baloglu, Ozal Yildirim, and U. Rajendra Acharya, Automated detection of COVID-19 cases using deep neural networks with X-ray images, Computers in Biology and Medicine 121 (2020), 103792 (en).
[17] K. Pandey, COVID-19 killed more people in 16 months than natural disasters in 20 years, DownToEarth (2021).
[18] H. Panwar, P.K. Gupta, M.K. Siddiqui, R. Morales-Menendez, and V. Singh, Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet, Chaos Solitons Fractals 138 (2020), 109944.
[19] A. Rehman, M.A. Iqbal, H. Xing, and I. Ahmed, COVID-19 detection empowered with machine learning and deep learning techniques: A systematic review, Appl. Sci. 11 (2021), no. 8, 3414.
[20] A. Rehman, S. Naz, A. Khan, A. Zaib, and I. Razzak, Improving coronavirus (COVID-19) diagnosis using deep transfer learning, preprint, Infectious Diseases (except HIV/AIDS), April 2020.
[21] E. Rezende, G. Ruppert, T. Carvalho, F. Ramos, and P. de Geus, Malicious software classification using transfer learning of ResNet-50 deep neural network, 16th IEEE Int. Conf. Machine Learn. Appl. (ICMLA) (Cancun, Mexico), IEEE, December 2017, pp. 1011–1014.
[22] S. Ruuska, W. H¨am¨al¨ainen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle, Behav. Process. 148 (2018), 56–62.
[23] P.Kumar Sethy and S.K. Behera, Detection of coronavirus disease (covid-19) based on deep features, (2020), Publisher: MDPI AG.
[24] C. Shorten and T.M. Khoshgoftaar, A survey on image data augmentation for deep learning, J. Big Data 6 (2019), no. 1, 60.
[25] N. Tajbakhsh, J.Y. Shin, S.R. Gurudu, R.T. Hurst, C.B. Kendall, M.B. Gotway, and J. Liang, Convolutional neural networks for medical image analysis: Full training or fine tuning?, IEEE Trans. Med. Imag. 35 (2016), no. 5, 1299–1312.
[26] M.M. Taresh, N. Zhu, T.A.A. Ali, A.S. Hameed, and M.L. Mutar, Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks, Int. J. Biomed. Imag. 2021 (2021), 1–9.
[27] D. Theckedath and R.R. Sedamkar, Detecting Affect States Using VGG16, ResNet50 and SE-ResNet50 Networks, SN Comput. Sci. 1 (2020), no. 2, 79.
[28] F. Ucar and D. Korkmaz, COVIDiagnosis-net: Deep bayes-queezenet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images, Med. Hypoth. 140 (2020), 109761.
[29] A. Uddin, B. Talukder, M. Monirujjaman Khan, and A. Zaguia, Study on Convolutional Neural Network to Detect COVID-19 from Chest X-Rays, Math. Prob. Engin. 2021 (2021), 1–11.
[30] A. Victor Ikechukwu, S. Murali, R. Deepu, and R.C. Shivamurthy, ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images, Global Transit. Proc. 2 (2021), no. 2, 375–381.
[31] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R.M. Summers, ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, 2017 IEEE Conf. Comput Vision Pattern Recogn (CVPR) (Honolulu, HI), IEEE, July 2017, pp. 3462–3471.
