How to cite this paper
Fachrel, J., Pravitasari, A., Nurma, I., Nurmansyah, M & Fajar, F. (2023). A comparison between CNN and combined CNN-LSTM for chest X-ray based COVID-19 detection.Decision Science Letters , 12(2), 199-210.
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Pravitasari, A.A., Iriawan, N., Almuhayar, M., Azmi, T., Irhamah, Fithriasari, K., Purnami, S. W., & Ferriastuti, W., (2020). UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. TELKOMNIKA, 18(3), 1310-1318.
Pustokhin, D. A., Pustokhina, I. V., Dinh, P. N., Phan, S. V., Nguyen, G. N., & Joshi, G. P. (2020). An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19. Journal of Applied Statistics, 1-18.
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Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S. B. A., ... & Chowdhury, M. E. (2021). Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Computers in biology and medicine, 132, 104319.
Reshi, A. A., Rustam, F., Mehmood, A., Alhossan, A., Alrabiah, Z., Ahmad, A., ... & Choi, G. S. (2021). An efficient CNN model for COVID-19 disease detection based on X-ray image classification. Complexity, 2021, 1-12.
Salman, F. M., Abu-Naser, S. S., Alajrami, E., Abu-Nasser, B. S., & Alashqar, B. A. (2020). Covid-19 detection using artificial intelligence
Santurkar, S., Tsipras, D., Ilyas, A., & Madry, A. (2018). How does batch normalization help optimization?. Advances in neural information processing systems, 31.
Setiawan, W. (2021, May). Character Recognition using Adjustment Convolutional Network with Dropout Layer. In IOP Conference Series: Materials Science and Engineering (Vol. 1125, No. 1, p. 012049). IOP Publishing.
Sheikh, A., Robertson, C., & Taylor, B. (2021). BNT162b2 and ChAdOx1 nCoV-19 vaccine effectiveness against death from the delta variant. New England Journal of Medicine, 385(23), 2195-2197.
Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221.
Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Lv, J. (2020). Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE transactions on cybernetics, 50(9), 3840-3854.
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., & Peng, Z. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Jama, 323(11), 1061-1069.
Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1-12.
WHO Coronavirus (COVID-19) Dashboard, https://covid19.who.int/ Retrieved 08 June, 2022.
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9(4), 611-629.
Yan, Y., Yao, X. J., Wang, S. H., & Zhang, Y. D. (2021). A survey of computer-aided tumor diagnosis based on convolutional neural network. Biology, 10(11), 1084.
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation, 31(7), 1235-1270.
Zein, A. (2021). Pendeteksian virus corona dalam gambar x-ray menggunakan algoritma artificial intelligence dengan deep learning python. Jurnal ESIT (E-Bisnis, Sistem Informasi, Teknologi Informasi), 15(1).
Zhou, C., Sun, C., Liu, Z., & Lau, F. (2015). A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630.
Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, August). Understanding of a convolutional neural network. In 2017 international conference on engineering and technology (ICET) (pp. 1-6). Ieee.
Aldi, M. W. P., Jondri, J., & Aditsania, A. (2018). Analisis dan Implementasi Long Short Term Memory Neural Network untuk Prediksi Harga Bitcoin. eProceedings of Engineering, 5(2).
Chowdhury, M.E.H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M.A., Mahbub, Z.B., Islam, K.R., Khan, M.S., Iqbal, A., Al-Emadi, N., Reaz, M.B.I., & Islam, M. T. (2020). Can AI help in screening Viral and COVID-19 pneumonia? IEEE Access, 8, 132665 - 132676.
Coronavirus disease - Answers. (n.d.). Retrieved from World Health Organization, https://www.who.int/ Retrieved 31 December, 2021
COVID-19 Radiography Database, https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database/version/4, Retrieved 14 June, 2022
Dastider, A. G., Sadik, F., & Fattah, S. A. (2021). An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound. Computers in Biology and Medicine, 132, 104296.
Demir, F. (2021). DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images. Applied Soft Computing, 103, 107160.
El Asnaoui, K., & Chawki, Y. (2021). Using X-ray images and deep learning for automated detection of coronavirus disease. Journal of Biomolecular Structure and Dynamics, 39(10), 3615-3626.
Gilanie, G., Bajwa, U. I., Waraich, M. M., Asghar, M., Kousar, R., Kashif, A., ... & Rafique, H. (2021). Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks. Biomedical Signal Processing and Control, 66, 102490.
Islam, M. Z., Islam, M. M., & Asraf, A. (2020). A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in medicine unlocked, 20, 100412.
Jie, H. J., & Wanda, P. (2020). RunPool: A Dynamic Pooling Layer for Convolution Neural Network. Int. J. Comput. Intell. Syst., 13(1), 66-76.
Kim, T. Y., & Cho, S. B. (2019). Predicting residential energy consumption using CNN-LSTM neural networks. Energy, 182, 72-81.
Liu, Y., Yu, X., Wu, Y., & Song, S. (2021). Forecasting Variation Trends of Stocks via Multiscale Feature Fusion and Long Short-Term Memory Learning. Scientific Programming, 2021.
Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN-LSTM-based model to forecast stock prices. Complexity, 2020, 1-10.
Narin, A., Kaya, C., & Pamuk, Z. (2021). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications, 24(3), 1207-1220.
Pravitasari, A.A., Iriawan, N., Almuhayar, M., Azmi, T., Irhamah, Fithriasari, K., Purnami, S. W., & Ferriastuti, W., (2020). UNet-VGG16 with transfer learning for MRI-based brain tumor segmentation. TELKOMNIKA, 18(3), 1310-1318.
Pustokhin, D. A., Pustokhina, I. V., Dinh, P. N., Phan, S. V., Nguyen, G. N., & Joshi, G. P. (2020). An effective deep residual network based class attention layer with bidirectional LSTM for diagnosis and classification of COVID-19. Journal of Applied Statistics, 1-18.
Qiu, J., Wang, B., & Zhou, C. (2020). Forecasting stock prices with long-short term memory neural network based on attention mechanism. PloS one, 15(1), e0227222.
Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S. B. A., ... & Chowdhury, M. E. (2021). Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Computers in biology and medicine, 132, 104319.
Reshi, A. A., Rustam, F., Mehmood, A., Alhossan, A., Alrabiah, Z., Ahmad, A., ... & Choi, G. S. (2021). An efficient CNN model for COVID-19 disease detection based on X-ray image classification. Complexity, 2021, 1-12.
Salman, F. M., Abu-Naser, S. S., Alajrami, E., Abu-Nasser, B. S., & Alashqar, B. A. (2020). Covid-19 detection using artificial intelligence
Santurkar, S., Tsipras, D., Ilyas, A., & Madry, A. (2018). How does batch normalization help optimization?. Advances in neural information processing systems, 31.
Setiawan, W. (2021, May). Character Recognition using Adjustment Convolutional Network with Dropout Layer. In IOP Conference Series: Materials Science and Engineering (Vol. 1125, No. 1, p. 012049). IOP Publishing.
Sheikh, A., Robertson, C., & Taylor, B. (2021). BNT162b2 and ChAdOx1 nCoV-19 vaccine effectiveness against death from the delta variant. New England Journal of Medicine, 385(23), 2195-2197.
Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221.
Sun, Y., Xue, B., Zhang, M., Yen, G. G., & Lv, J. (2020). Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE transactions on cybernetics, 50(9), 3840-3854.
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., & Peng, Z. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Jama, 323(11), 1061-1069.
Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1-12.
WHO Coronavirus (COVID-19) Dashboard, https://covid19.who.int/ Retrieved 08 June, 2022.
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9(4), 611-629.
Yan, Y., Yao, X. J., Wang, S. H., & Zhang, Y. D. (2021). A survey of computer-aided tumor diagnosis based on convolutional neural network. Biology, 10(11), 1084.
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation, 31(7), 1235-1270.
Zein, A. (2021). Pendeteksian virus corona dalam gambar x-ray menggunakan algoritma artificial intelligence dengan deep learning python. Jurnal ESIT (E-Bisnis, Sistem Informasi, Teknologi Informasi), 15(1).
Zhou, C., Sun, C., Liu, Z., & Lau, F. (2015). A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630.