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Smart and Automated Diagnosis of COVID-19 Using Artificial Intelligence Techniques

Masoud Alajmi1,*, Osama A. Elshakankiry2, Walid El-Shafai3, Hala S. El-Sayed4, Ahmed I. Sallam5, Heba M. El-Hoseny6, Ahmed Sedik7, Osama S. Faragallah2

1 Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
2 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
4 Department of Electrical Engineering, Faculty of Engineering, Menoufia University, Shebin El-Kom, 32511, Egypt
5 Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
6 Department of Electronics and Electrical Communication Engineering, Al-Obour High Institute for Engineering and Technology, Al-Obour, 3036, Egypt
7 Department of the Robots and Intelligent Machines, Faculty of AI, Kafrelsheikh University, Egypt

* Corresponding Author: Masoud Alajmi. Email: email

Intelligent Automation & Soft Computing 2022, 32(3), 1403-1413.


Machine Learning (ML) techniques have been combined with modern technologies across medical fields to detect and diagnose many diseases. Meanwhile, given the limited and unclear statistics on the Coronavirus Disease 2019 (COVID-19), the greatest challenge for all clinicians is to find effective and accurate methods for early diagnosis of the virus at a low cost. Medical imaging has found a role in this critical task utilizing a smart technology through different image modalities for COVID-19 cases, including X-ray imaging, Computed Tomography (CT) and magnetic resonance image (MRI) that can be used for diagnosis by radiologists. This paper combines ML with imaging analysis in an artificial deep learning approach for COVID-19 detection. The proposed methodology is based on convolutional long short term memory (ConvLSTM) to diagnose COVID-19 automatically from X-ray images. The main features are extracted from regions of interest in the medical images, and an intelligent classifier is used for the classification task. The proposed model has been tested on a dataset of X-ray images for COVID-19 and normal cases to evaluate the detection performance. The ConvLSTM model has achieved the desired results with high accuracy of 91.8%, 95.7%, 97.4%, 97.7% and 97.3% at 10, 20, 30, 40 and 50 epochs that will detect COVID-19 patients and reduce the medical diagnosis workload.


Cite This Article

M. Alajmi, O. A. Elshakankiry, W. El-Shafai, H. S. El-Sayed, A. I. Sallam et al., "Smart and automated diagnosis of covid-19 using artificial intelligence techniques," Intelligent Automation & Soft Computing, vol. 32, no.3, pp. 1403–1413, 2022.

cc 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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