Concatenation of Pre-Trained Convolutional Neural Networks for an Enhanced Corona Virus Screening Using Transfer Learning Technique

El Gannour, Oussama and Hamida, Soufiane and Cherradi, Bouchaib and Al-Sarem, Mohammed and Raihani, Abdelhadi and Saeed, Faisal and Hadwan, Mohammed (2021) Concatenation of Pre-Trained Convolutional Neural Networks for an Enhanced Corona Virus Screening Using Transfer Learning Technique. Electronics, 11 (1). p. 103. ISSN 2079-9292

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Abstract

Coronavirus is the most prevalent coronavirus infection with respiratory symptoms such as fever; cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, Coronavirus has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of Coronavirus is extremely important for the medical community to limit its spread. For large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish Coronavirus precisely in Chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., Coronavirus case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to con-firm the reliability of the proposed method for identifying the patients with Coronavirus disease from X-ray images. The proposed system was trained on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and Coronavirus cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%.

Item Type: Article
Identification Number: https://doi.org/10.3390/electronics11010103
Dates:
DateEvent
27 December 2021Accepted
29 December 2021Published Online
Uncontrolled Keywords: coronavirus; COVID-19; transfer learning; convolutional neural network; machine learning; concatenation technique; feature extraction
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Faisal Saeed
Date Deposited: 05 Jan 2022 11:41
Last Modified: 05 Jan 2022 11:43
URI: http://www.open-access.bcu.ac.uk/id/eprint/12581

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