Implementation of Transfer Learning for Covid-19 and Pneumonia Disease Detection Through Chest X-Rays Based on Web

Nindya Eka Apsari, Sugiyanto Sugiyanto, Sri Sulistijowati Handajani

Abstract

Coronavirus disease 2019, known as COVID-19, attacks the human respiratory system caused by severe acute respiratory syndrome coronavirus-2 (SARS-Cov-2). COVID-19 disease and pneumonia show similar symptoms such as fever, cough, even headache. Diagnosis of pneumonia can be tested through diagnostic tests, including blood tests, chest X-rays, and pulse oximetry, while the diagnosis of COVID-19 recommended by WHO is with swab test (RT-PCR). But in fact, the swab test method takes a relatively long time, for about one to seven days, for the result, and is not cheap. For that, there needs to be a development that can be one of the options in diagnosing COVID-19 and pneumonia at once, especially since both diseases have similar symptoms. One option that can be done is the diagnosis using a chest X-ray. This research aims to detect COVID-19 disease and pneumonia through chest X-rays using transfer learning to increase the accuracy of disease diagnosis with a more efficient time. The architecture used is EfficientNet B0 with variations in optimization parameters, learning rates, and epochs. EfficientNet B0 Adam optimization with a learning rate of 0.001 in the 6th epochs is a great model that we obtained. Furthermore, the evaluation of the model got accuracy, precision, recall, and f1-score of 92%. Then the model visualization is done using Grad-CAM. To implement the best model, web application development is done to make it easier to detect COVID-19 disease and pneumonia.

Keywords: COVID-19; pneumonia; EfficientNet; transfer learning; web

Full Text:

PDF

References

A. E. Thompson, “Pneumonia is a lung infection that can be caused by bacteria, viruses, or fungi,” Jama Patient Page “Pulmonary,” vol. 315, no. 6, p. 2016, 2016.

A. Zein, “Pendeteksian VIirus Corona Dalam Gambar X-Ray Menggunakan Algoritma Artifical Intelligence Dengan Deep Learning Python,” J. Teknol. Inf. ESIT, vol. XV, no. 01, pp. 19–23, 2020.

J. Estomihi, Klasifikasi Penyakit Pneumonia dari Citra X-Ray Menggunakan Backpropagation, Repos. Institusi USU, 2019.

L. Tobias, A. Ducournau, F. Rousseau, G. Mercier, and R. Fablet, “Convolutional Neural Networks for object recognition on mobile devices: A case study,” Proc. - Int. Conf. Pattern Recognit., vol. 0, pp. 3530–3535, 2016, doi: 10.1109/ICPR.2016.7900181.

H. W. Ng, V. D. Nguyen, V. Vonikakis, and S. Winkler, “Deep learning for emotion recognition on small datasets using transfer learning,” ICMI 2015 - Proc. 2015 ACM Int. Conf. Multimodal Interact., pp. 443–449, 2015, doi: 10.1145/2818346.2830593.

M. Singh, S. Bansal, S. Ahuja, R. K. Dubey, B. K. Panigrahi, and N. Dey, “Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data,” Med. Biol. Eng. Comput., vol. 59, no. 4, pp. 825–839, 2021, doi: 10.1007/s11517-020-02299-2.

R. Ardiawan, T. Rakasiwi, G. R. Maulana, and N. Yudistira, “Aplikasi Deteksi Covid-19 dan Pneumonia melalui Citra X-ray Dada menggunakan Residual Convolutional Neural Network,” Researchgate, no. January, 2021, doi: 10.13140/RG.2.2.16582.91202.

L. T. Duong, P. T. Nguyen, C. Di Sipio, and D. Di Ruscio, “Automated fruit recognition using EfficientNet and MixNet,” Comput. Electron. Agric., vol. 171, no. February, p. 105326, 2020, doi: 10.1016/j.compag.2020.105326.

Z. Li and D. Hoiem, “Learning without Forgetting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 12, pp. 2935–2947, 2018, doi: 10.1109/TPAMI.2017.2773081.

R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization,” Rev. do Hosp. das Cl??nicas, vol. 17, pp. 331–336, 2016, [Online]. Available: http://arxiv.org/abs/1610.02391.

DOI: https://doi.org/10.13057/ijas.v5i1.59442

Refbacks

  • There are currently no refbacks.