COVID-19: a new deep learning computer-aided model for classification

• Published in: PeerJ. Computer science Vol. 7; p. e358
• Main Authors: Elzeki, Omar M., Shams, Mahmoud, Sarhan, Shahenda, Abd Elfattah, Mohamed, Hassanien, Aboul Ella
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 18.02.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Bioinformatics; CAI; Chest; China; Classification; Computational Biology; Computer assisted instruction; Computer Vision; Coronaviruses; COVID-19; Data Mining and Machine Learning; Datasets; Decision making; Deep convolutional neural network; Deep learning; Disease; Epidemics; Equipment and supplies; Gray scale; Machine learning; Medical research; Medical screening; Model accuracy; Pandemics; Pneumonia; Teaching; Viral diseases; Viruses; X ray imagery; X-ray images; X-rays; China; COVID-19; Deep convolutional neural network; X-ray images; Classification
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.358

Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F 1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.