A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a singl
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RESEARCH PAPER
A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell Nour Eldeen M. Khalifa & Mohamed Hamed N. Taha & Gunasekaran Manogaran & Mohamed Loey
Received: 27 July 2020 / Accepted: 6 October 2020 # Springer Nature B.V. 2020
Abstract Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARSCoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be This article is part of the topical collection: Role of Nanotechnology and Internet of Things in Healthcare Guest Editors: Florian Heberle, Steve bull and John Fitzgerald N. E. M. Khalifa : M. H. N. Taha Department of Information Technology, Faculty of Computers & Artificial Intelligence, Cairo University, Cairo 12613, Egypt
N. E. M. Khalifa e-mail: [email protected] M. H. N. Taha e-mail: [email protected] G. Manogaran University of California, Davis, USA e-mail: [email protected]
presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction. Keywords COVID-19 . Deep transfer learning . Classical machine learning
G. Manogaran College of Information and Electrical Engineering, Asia University, Taichung, Taiwan M. Loey (*) Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt e-mail: [email protected]
Introduction SARS virus spread around the world and caused a lot of panic globally at the end of February 2003 (Chang et al.
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J Nanopart Res
(2020) 22:313
model and machine learning methods for the classification of
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