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A Transfer Learning-Enabled Optimized Extreme Deep Learning Paradigm for Diagnosis of COVID-19

Ahmed Reda*, Sherif Barakat, Amira Rezk

Department of Information System, Faculty of Computer and Information Science, Mansoura University, Mansoura, Egypt

* Corresponding Author: Ahmed Reda. Email:

(This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2022, 70(1), 1381-1399.


Many respiratory infections around the world have been caused by coronaviruses. COVID-19 is one of the most serious coronaviruses due to its rapid spread between people and the lowest survival rate. There is a high need for computer-assisted diagnostics (CAD) in the area of artificial intelligence to help doctors and radiologists identify COVID-19 patients in cloud systems. Machine learning (ML) has been used to examine chest X-ray frames. In this paper, a new transfer learning-based optimized extreme deep learning paradigm is proposed to identify the chest X-ray picture into three classes, a pneumonia patient, a COVID-19 patient, or a normal person. First, three different pre-trained Convolutional Neural Network (CNN) models (resnet18, resnet25, densenet201) are employed for deep feature extraction. Second, each feature vector is passed through the binary Butterfly optimization algorithm (bBOA) to reduce the redundant features and extract the most representative ones, and enhance the performance of the CNN models. These selective features are then passed to an improved Extreme learning machine (ELM) using a BOA to classify the chest X-ray images. The proposed paradigm achieves a 99.48% accuracy in detecting covid-19 cases.


Cite This Article

A. Reda, S. Barakat and A. Rezk, "A transfer learning-enabled optimized extreme deep learning paradigm for diagnosis of covid-19," Computers, Materials & Continua, vol. 70, no.1, pp. 1381–1399, 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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