Reduction of Feature Extraction for COVID-19 CXR using Depthwise Separable Convolution Network

Keywords: CNN, COVID-19 CXR, Transfer Learning, Feature Extraction, Depthwise Separable Convolution Network.


A Convolutional Neural Network (CNN) classifier is generally utilized to classify an image tensor according to the mapped labels. The simplification of the classifier causes CNN to be often used to classify images, especially in the biomedical field. Thus, CNN is widely used to classify computer tomography (CT) and chest X-ray (CXR) images against the mapped labels. Several transfer learning models were implemented to classify CXR images for preliminary detection of COVID-19 infection, e.g., ResNet, Inception, Xception, etc. However, a transfer learning model has a maximum and minimum input resolution. Thus, the computational cost tends to be huge and unable to be optimized. Therefore, A custom CNN model can be a solution to reduce computational costs by configuring the feature extraction layers. This study proposed an efficient reduction of feature extraction for COVID-19 CXR namely Depthwise Separable Convolution Network. Furthermore, numerous strategies were adopted to lower the computational cost while retaining accuracy, including customizing the Batch Normalization (BN) layer and replacing the convolution layer with a separable convolution layer. The proposed model successfully reduced the feature extraction represented by the decreases in trainable parameters from 28.640 trainable parameters to 4.640 trainable parameters. The depthwise separable convolution effectively retains the performance accuracy 72.96%, loss 12.43%, recall 74.67%, precision 77.67%, and F1-score 75.33%. The CXR augmentation is also successfully increase the performance accuracy 74.55%, loss 11.37%, recall 77.67%, precision 79.56%, and F1-score 78.33%.


Download data is not yet available.


[1] J. Zhao, X. He, X. Yang, Y. Zhang, S. Zhang, and P. Xie, “COVID-CT-Dataset: A CT image dataset about COVID-19,” arXiv, pp. 1–14, 2020.
[2] S. Hassantabar, M. Ahmadi, and A. Sharifi, “Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches,” Chaos, Solitons and Fractals, vol. 140, p. 110170, 2020, doi: 10.1016/j.chaos.2020.110170.
[3] L. Wang, Z. Q. Lin, and A. Wong, “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Sci. Rep., vol. 10, no. 1, pp. 1–12, 2020, doi: 10.1038/s41598-020-76550-z.
[4] N. K. Chowdhury, M. M. Rahman, and M. A. Kabir, “PDCOVIDNeT: A parallel-dilated convolutional neural network architecture for detecting COVID-19 from chest X-ray images,” arXiv, 2020, doi: 10.1007/s13755-020-00119-3.
[5] Y. Zhong, “Using Deep Convolutional Neural Networks to Diagnose COVID-19 from Chest X-Ray Images,” arXiv, 2020.
[6] E. E. D. Hemdan, M. A. Shouman, and M. E. Karar, “COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images,” arXiv, 2020.
[7] M. A. Zulkifley, S. R. Abdani, and N. H. Zulkifley, “COVID-19 screening using a lightweight convolutional neural network with generative adversarial network data augmentation,” Symmetry (Basel)., vol. 12, no. 9, pp. 1–17, 2020, doi: 10.3390/SYM12091530.
[8] M. Rahimzadeh and A. Attar, “a New Modified Deep Convolutional Neural Network for Detecting Covid-19 From X-Ray Images,” arXiv, vol. 19, p. 100360, 2020, doi: 10.1016/j.imu.2020.100360.
[9] J. C. Y. Yujin Oh, Sangjoon Park, “Deep Learning Covid-19 Features on CXR using Limited Training Data Sets.” IEEE Transaction on Medical Imaging, Vol 39, No 8, pp. 2688–2700, 2020.
[10] S. Widodo et al., “Detection of Covid-19 on X-Ray Images Using a Deep Learning Convolution Neural Network,” pp. 255–259, 2021.
[11] Z. Iklima, T. M. Kadarina, and E. Ihsanto, “Realistic Image Synthesis of Covid-19 Chest X-Ray Imaging using Depthwise Boundary Equilibrium Generative Adversarial Networks,” vol. 9, no. 3, pp. 1–11, 2021, doi: 10.11591/eei.v9i3.xxxx.
[12] “Chest X-ray (Covid-19 & Pneumonia) | Kaggle.” (accessed May 12, 2022).
[13] D. Singh, V. Kumar, Vaishali, and M. Kaur, “Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks,” Eur. J. Clin. Microbiol. Infect. Dis., vol. 39, no. 7, pp. 1379–1389, 2020, doi: 10.1007/s10096-020-03901-z.
[14] B. Liu, D. Zou, L. Feng, S. Feng, P. Fu, and J. Li, “An FPGA-based CNN accelerator integrating depthwise separable convolution,” Electron., vol. 8, no. 3, 2019, doi: 10.3390/electronics8030281.
[15] A. H. Panahi, A. Rafiei, and A. Rezaee, “FCOD: Fast COVID-19 Detector based on deep learning techniques,” Informatics Med. Unlocked, vol. 22, p. 100506, 2021, doi: 10.1016/j.imu.2020.100506.
[16] L. Dang, P. Pang, and J. Lee, “Depth-wise separable convolution neural network with residual connection for hyperspectral image classification,” Remote Sens., vol. 12, no. 20, pp. 1–20, 2020, doi: 10.3390/rs12203408.
[17] D. Hossain, M. H. Imtiaz, T. Ghosh, V. Bhaskar, and E. Sazonov, “Real-Time Food Intake Monitoring Using Wearable Egocnetric Camera,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2020-July, no. March 2021, pp. 4191–4195, 2020, doi: 10.1109/EMBC44109.2020.9175497.
[18] D. S. Kermany et al., “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell, vol. 172, no. 5, pp. 1122-1131.e9, Feb. 2018, doi: 10.1016/J.CELL.2018.02.010.
[19] P. Saha and S. Neogy, “Concat_CNN: A Model to Detect COVID-19 from Chest X-ray Images with Deep Learning,” vol. 3, p. 305, 2022, doi: 10.1007/s42979-022-01182-1.
[20] S. Sukegawa et al., “Deep neural networks for dental implant system classification,” Biomolecules, vol. 10, no. 7, pp. 1–13, 2020, doi: 10.3390/biom10070984.
How to Cite
Z. Iklima, T. M. Kadarina, and R. Priambodo, “Reduction of Feature Extraction for COVID-19 CXR using Depthwise Separable Convolution Network ”,, vol. 4, no. 4, pp. 204-209, Oct. 2022.
Research Paper