OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19
- PDF / 2,341,161 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 80 Downloads / 208 Views
OptCoNet: an optimized convolutional neural network for an automatic diagnosis of COVID-19 Tripti Goel 1 & R. Murugan 1
&
Seyedali Mirjalili 2 & Deba Kumar Chakrabartty 3
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The quick spread of coronavirus disease (COVID-19) has become a global concern and affected more than 15 million confirmed patients as of July 2020. To combat this spread, clinical imaging, for example, X-ray images, can be utilized for diagnosis. Automatic identification software tools are essential to facilitate the screening of COVID-19 using X-ray images. This paper aims to classify COVID-19, normal, and pneumonia patients from chest X-ray images. As such, an Optimized Convolutional Neural network (OptCoNet) is proposed in this work for the automatic diagnosis of COVID-19. The proposed OptCoNet architecture is composed of optimized feature extraction and classification components. The Grey Wolf Optimizer (GWO) algorithm is used to optimize the hyperparameters for training the CNN layers. The proposed model is tested and compared with different classification strategies utilizing an openly accessible dataset of COVID-19, normal, and pneumonia images. The presented optimized CNN model provides accuracy, sensitivity, specificity, precision, and F1 score values of 97.78%, 97.75%, 96.25%, 92.88%, and 95.25%, respectively, which are better than those of state-of-the-art models. This proposed CNN model can help in the automatic screening of COVID-19 patients and decrease the burden on medicinal services frameworks. Keywords Automatic diagnosis . Coronavirus . COVID-19 . Convolutional neural network . Grey wolf optimizer . Stochastic gradient descent
1 Introduction Severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) is a novel virus that is enveloped with a large, single-stranded RNA genome. It emerged in Wuhan, China in December 2019 and caused the greatest pandemic of the millennium [1]. According to the World Health Organization (WHO) report, the total number of people infected by the disease as of 27 July 2020 is 16,114,449 with 646,641 deaths. The typical symptoms of the disease are fever, breathlessness, cough, fatigue, and loss of taste and smell [2]. The standard
* R. Murugan [email protected] 1
Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, Assam 788010, India
2
Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006, Australia
3
Department of Radiology, Silchar Medical College and Hospital, Silchar, Assam 788014, India
method for diagnosing COVID-19 is reverse transcriptionpolymerase chain reaction from a nasopharyngeal swab. Even though the continuous polymerase chain reaction examination of the sputum has the best quality for detecting COVID-19, the time required to confirm COVID-19 in infected patients can be high given the elevated false positive results of the examination [3]. Therefore, clinical ima
Data Loading...
