Analyzing the Effect of Basic Data Augmentation for COVID-19 Detection through a Fractional Factorial Experimental Design
Abstract
Doi: 10.28991/ESJ-2023-SPER-01
Full Text: PDF
Keywords
References
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. doi:10.1007/s10044-021-00984-y.
Elgendi, M., Nasir, M. U., Tang, Q., Smith, D., Grenier, J.-P., Batte, C., Spieler, B., Leslie, W. D., Menon, C., Fletcher, R. R., Howard, N., Ward, R., Parker, W., & Nicolaou, S. (2021). The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective. Frontiers in Medicine, 8. doi:10.3389/fmed.2021.629134.
Ji, T., Liu, Z., Wang, G. Q., Guo, X., Akbar khan, S., Lai, C., Chen, H., Huang, S., Xia, S., Chen, B., Jia, H., Chen, Y., & Zhou, Q. (2020). Detection of COVID-19: A review of the current literature and future perspectives. Biosensors and Bioelectronics, 166, 112455. doi:10.1016/j.bios.2020.112455.
Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. 2017 International Conference on Engineering and Technology (ICET). doi:10.1109/icengtechnol.2017.8308186.
Baldeon-Calisto, M., & Lai-Yuen, S. K. (2020). AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation. Neurocomputing, 392, 325–340. doi:10.1016/j.neucom.2019.01.110.
Baldeon Calisto, M., & Lai-Yuen, S. K. (2020). AdaEn-Net: An ensemble of adaptive 2D–3D Fully Convolutional Networks for medical image segmentation. Neural Networks, 126, 76–94. doi:10.1016/j.neunet.2020.03.007.
Baldeon Calisto, M., & Lai-Yuen, S. K. (2021). EMONAS-Net: Efficient multiobjective neural architecture search using surrogate-assisted evolutionary algorithm for 3D medical image segmentation. Artificial Intelligence in Medicine, 119, 102154. doi:10.1016/j.artmed.2021.102154.
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), 19549. doi:10.1038/s41598-020-76550-z.
Monshi, M. M. A., Poon, J., Chung, V., & Monshi, F. M. (2021). CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR. Computers in Biology and Medicine, 133, 104375. doi:10.1016/j.compbiomed.2021.104375.
Algarni, A. D., El-Shafai, W., El Banby, G. M., Abd El-Samie, F. E., & Soliman, N. F. (2022). An efficient CNN-based hybrid classification and segmentation approach for COVID-19 detection. Computers, Materials and Continua, 70(3), 4393–4410. doi:10.32604/cmc.2022.020265.
Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020). Covid-19 image data collection: Prospective predictions are the future. arXiv preprint arXiv:2006.11988. doi:10.48550/arXiv.2006.11988.
Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.195.
Khan, A. I., Shah, J. L., & Bhat, M. M. (2020). CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods and Programs in Biomedicine, 196, 105581. doi:10.1016/j.cmpb.2020.105581.
Taylor, L., & Nitschke, G. (2018). Improving Deep Learning with Generic Data Augmentation. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). doi:10.1109/ssci.2018.8628742.
Mikolajczyk, A., & Grochowski, M. (2018). Data augmentation for improving deep learning in image classification problem. 2018 International Interdisciplinary PhD Workshop (IIPhDW). doi:10.1109/iiphdw.2018.8388338.
Shijie, J., Ping, W., Peiyi, J., & Siping, H. (2017). Research on data augmentation for image classification based on convolution neural networks. 2017 Chinese Automation Congress (CAC). doi:10.1109/cac.2017.8243510.
Perez, F., Vasconcelos, C., Avila, S., Valle, E. (2018). Data Augmentation for Skin Lesion Analysis. In: , et al. OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis. CARE CLIP OR 2.0 ISIC 2018. Lecture Notes in Computer Science, 11041. Springer, Cham, Switzerland. doi:10.1007/978-3-030-01201-4_33.
Safdar, M., Kobaisi, S., & Zahra, F. (2020). A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor. Acta Informatica Medica, 28(1), 29. doi:10.5455/aim.2020.28.29-36.
Omigbodun, A. O., Noo, F., McNitt-Gray, M., Hsu, W., & Hsieh, S. S. (2019). The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction. Medical Physics, 46(10), 4563–4574. doi:10.1002/mp.13755.
Zargari Khuzani, A., Heidari, M., & Shariati, S. A. (2021). COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images. Scientific Reports, 11(1), 9887. doi:10.1038/s41598-021-88807-2.
Kermany, D., Zhang, K., & Goldbaum, M. (2018). Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley data, 2(2). doi:10.17632/RSCBJBR9SJ.2.
Chlap, P., Min, H., Vandenberg, N., Dowling, J., Holloway, L., & Haworth, A. (2021). A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology, 65(5), 545–563. doi:10.1111/1754-9485.13261.
Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on Image Data Augmentation for Deep Learning. Journal of Big Data, 6(1), 60. doi:10.1186/s40537-019-0197-0.
Abbas, A., Abdelsamea, M. M., & Gaber, M. M. (2021). Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Applied Intelligence, 51(2), 854–864. doi:10.1007/s10489-020-01829-7.
Baldeon calisto, M., Balseca Zurita, J. S., & Cruz Patiño, M. A. (2021). COVID-19 ResNet: Residual neural network for COVID-19 classification with bayesian data augmentation. ACI Avances En Ciencias e Ingenierías, 13(2), 19. doi:10.18272/aci.v13i2.2288.
Chowdhury, N. K., Rahman, Md. M., & Kabir, M. A. (2020). PDCOVIDNet: a parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images. Health Information Science and Systems, 8(1). doi:10.1007/s13755-020-00119-3.
Goel, T., Murugan, R., Mirjalili, S., & Chakrabartty, D. K. (2021). OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19. Applied Intelligence, 51(3), 1351–1366. doi:10.1007/s10489-020-01904-z.
Kumar, A., Tripathi, A. R., Satapathy, S. C., & Zhang, Y. D. (2022). SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network. Pattern Recognition, 122, 108255. doi:10.1016/j.patcog.2021.108255.
Marques, G., Agarwal, D., & de la Torre Díez, I. (2020). Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network. Applied Soft Computing Journal, 96, 106691. doi:10.1016/j.asoc.2020.106691.
Nishio, M., Noguchi, S., Matsuo, H., & Murakami, T. (2020). Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods. Scientific Reports, 10(1). doi:10.1038/s41598-020-74539-2.
Rahimzadeh, M., & Attar, A. (2020). A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Informatics in Medicine Unlocked, 19(100360). doi:10.1016/j.imu.2020.100360.
Yoo, S. H., Geng, H., Chiu, T. L., Yu, S. K., Cho, D. C., Heo, J., Choi, M. S., Choi, I. H., Cung Van, C., Nhung, N. V., Min, B. J., & Lee, H. (2020). Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging. Frontiers in Medicine, 7. doi:10.3389/fmed.2020.00427.
Montgomery, D. (2019). Design and Analysis of Experiments (10th Ed.). Wiley, Hoboken, United States.
Lujan-Moreno, G. A., Howard, P. R., Rojas, O. G., & Montgomery, D. C. (2018). Design of experiments and response surface methodology to tune machine learning hyperparameters, with a random forest case-study. Expert Systems with Applications, 109, 195–205. doi:10.1016/j.eswa.2018.05.024.
Staelin, C. (2003). Parameter selection for support vector machines. Hewlett-Packard Company, Tech. Rep. HPL-2002-354R1, 1. HP Laboratories, Haifa, Israel.
Chou, F. I., Tsai, Y. K., Chen, Y. M., Tsai, J. T., & Kuo, C. C. (2019). Optimizing Parameters of Multi-Layer Convolutional Neural Network by Modeling and Optimization Method. IEEE Access, 7, 68316–68330. doi:10.1109/ACCESS.2019.2918563.
Fang, K. T., & Lin, D. K. J. (2003). Ch. 4. Uniform experimental designs and their applications in industry. Handbook of Statistics, 22, 131–170, Elsevier, Amsterdam, Netherlands. doi:10.1016/S0169-7161(03)22006-X.
Ahrens, W. H., Cox, D. J., & Budhwar, G. (1990). Use of the Arcsine and Square Root Transformations for Subjectively Determined Percentage Data. Weed Science, 38(4–5), 452–458. doi:10.1017/s0043174500056824.
Wodzinski, M., Banzato, T., Atzori, M., Andrearczyk, V., Cid, Y. D., & Muller, H. (2020). Training Deep Neural Networks for Small and Highly Heterogeneous MRI Datasets for Cancer Grading. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine; Biology Society (EMBC). doi:10.1109/embc44109.2020.9175634.
Ogawa, R., Kido, T., & Mochizuki, T. (2019). Effect of augmented datasets on deep convolutional neural networks applied to chest radiographs. Clinical Radiology, 74(9), 697–701. doi:10.1016/j.crad.2019.04.025.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. doi:10.1145/3065386.
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2019). AutoAugment: Learning Augmentation Strategies From Data. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). bdoi:10.1109/cvpr.2019.00020.
DOI: 10.28991/ESJ-2023-SPER-01
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Maria Baldeon Calisto, Juan Jose Murillo, Bernardo Puente-Mejia, Danny Navarrete, Daniel Riofrío, Noel Peréz, Diego Benítez, Ricardo Flores Moyano