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Automated Deep Learning of COVID-19 and Pneumonia Detection Using Google AutoML

Saiful Izzuan Hussain*, Nadiah Ruza

Department of Mathematical Sciences, Faculty of Science and Technology Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia

* Corresponding Author: Saiful Izzuan Hussain. Email:

(This article belongs to this Special Issue: New Trends in Artificial Intelligence and Deep learning for Instrumentation, Sensors, and Robotics)

Intelligent Automation & Soft Computing 2022, 31(2), 1143-1156.


Coronavirus (COVID-19) is a pandemic disease classified by the World Health Organization. This virus triggers several coughing problems (e.g., flu) that include symptoms of fever, cough, and pneumonia, in extreme cases. The human sputum or blood samples are used to detect this virus, and the result is normally available within a few hours or at most days. In this research, we suggest the implementation of automated deep learning without require handcrafted expertise of data scientist. The model developed aims to give radiologists a second-opinion interpretation and to minimize clinicians’ workload substantially and help them diagnose correctly. We employed automated deep learning via Google AutoML for COVID-19 X-ray detection to provide an automated and faster diagnosis. The model is employed on X-ray images to detect COVID-19 based on several binary and multi-class cases. It consists of three scenarios of binary classification categorized as healthy, pneumonia, and COVID-19. The multi-classification model based on these three labels is employed to differentiate between them directly. An investigational review of 1125 chest X-rays indicates the efficiency of the proposed method. AutoML enables binary and multi-classification tasks to be performed with an accuracy up to 98.41%.


Cite This Article

S. Izzuan Hussain and N. Ruza, "Automated deep learning of covid-19 and pneumonia detection using google automl," Intelligent Automation & Soft Computing, vol. 31, no.2, pp. 1143–1156, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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