Covid 19 image classification using hybrid averaging transfer learning model

Qamar Abbas; Khalid Mahmood; Saif ur Rehman; Muhammad Imran

doi:10.22581/muet1982.2304.2900

Author(s)

Qamar Abbas Department of Computer Science, Faculty of Computing and Information Technology, International Islamic University,Islamabad,Pakistan

Khalid Mahmood Institute of Computing and Information Technology, Gomal University,D.I.Khan, KP,Pakistan

Saif ur Rehman University Institute of Information Technology, PMAS Arid Agriculture University,Rawalpindi,Pakistan

Muhammad Imran Department of Robotics and Artificial Intelligence, SZABIST University Islamabad, Pakistan

Abstract

The outbreak of Corona Virus 2019(Covid-19) is a great threat to the whole world. It is crucial to early detect patients infected with covid-19 and treat them to mitigate the rapid spread of this disease. It is an immediate priority to overcome the traditional screening and develop an accurate as well as speedy covid-19 automatic diagnosis system. Computer Tomography (CT) and Chest X-Ray imaging coupled with deep learning models to develop and test Computer Aided Screening (CAS) of covid-19 images from the normal images. In this paper classification and screening of covid-19 disease are performed by using pre-trained convolutional neural networks and a proposed hybrid model on an available standard dataset of chest X-Ray images. The proposed hybrid model employs the pre-trained Convolutional Neural Network models and Transfer Learning models. Our proposed model consists of three stages where extraction of features is performed in first stage by using pre-trained machine learning model. Deep features are extracted by using the infusion of the Transfer Learning Technique in the second stage of the model. The third stage uses Flatten and Classification layers to diagnose of Covid-19 patients. In order to assure the consistency of the proposed model, by considering standard dataset XRay images. Simulation results of performance metrics of Accuracy, F1 Score, Precision, Recall, ROC, and AUC curve, and training and testing loss are used to evaluate and compare the proposed model with existing models. Experimental result demonstrates that the hybrid model improves the screening process for Covid-19 disease by achieving higher accuracy.

Publication Details

Page(s) 72-83

DOI 10.22581/muet1982.2304.2900

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 42, Issue: 4, Year: 2023

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Keywords

Image Classification Covid-19 Transfer Learning Hybrid Model Averaging