Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases
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Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. https://arxiv.org/abs/2003.10849.
Sethy, P. K., & Behera, S. K. (2020). Detection of coronavirus disease (COVID-19) based on deep features.
Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine. https://doi.org/10.1007/s13246-020-00865.
Greenspan, H., Van Ginneken, B., & Summers, R. M. (2016). Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging,35(5), 1153–1159.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. London: MIT Press.
Zhang, Q., & Zhu, S.-C. (2018). Visual interpretability for deep learning: A survey. Frontiers of Information Technology & Electronic Engineering,19(1), 27–39.
Cohen, J.P., Morrison, P., Dao, L. (2020). COVID-19 image data collection. https://arxiv.org/abs/2003.11597.
S.I. S. o. M. a. I. Radiology. (2020). COVID-19 database. Retrieved from https://www.sirm.org/category/senza-categoria/covid-19/.
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., et al. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell,172(5), 1122–1131.e9. https://doi.org/10.1016/j.cell.2018.02.010.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017). Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2097–2106).
Wang, J., & Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks,11, 1–8.
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., et al. (2016). Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging,35(5), 1285–1298.
Wang, G., Li, W., Zuluaga, M. A., Pratt, R., Patel, P. A., Aertsen, M., et al. (2018). Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Transactions on Medical Imaging,37(7), 1562–1573.
Yang, Y., Yan, L.-F., Zhang, X., Han, Y., Nan, H.-Y., Hu, Y.-C., et al. (2018). Glioma grading on conventional MR images: A deep learning study with transfer learning. Frontiers in Neuroscience,12, 804. https://doi.org/10.3389/fnins.2018.00804.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Adam, H., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. Retrieved from https://arxiv.org/abs/1704.04861.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251–1258).
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-first AAAI conference on artificial intelligence.
Mummadi, S. R., Al-Zubaidi, A., & Hahn, P. Y. (2018). Overfitting and use of mismatched cohorts in deep learning models: Preventable design limitations. American Journal of Respiratory and Critical Care Medicine,198(4), 544–545.
Santurkar, S., Tsipras, D., Ilyas, A., & Madry, A. (2018). How does batch normalization help optimization? In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems (pp. 2483–2493). Cambridge: MIT Press.
Gal, Y., & Ghahramani, Z. (2016). A theoretically grounded application of dropout in recurrent neural networks. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems (pp. 1019–1027). Cambridge: MIT Press.
Chollet, F., et al. (2015). Keras. Retrieved from https://keras.io
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., & Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Retrieved from https://www.tensorflow.org/.
Choi, W., Oh, J. H., Riyahi, S., Liu, C.-J., Jiang, F., Chen, W., et al. (2018). Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Medical Physics,45(4), 1537–1549.
Kohli, M., Prevedello, L. M., Filice, R. W., & Geis, J. R. (2017). Implementing machine learning in radiology practice and research. American Journal of Roentgenology,208(4), 754–760.