Peer Review #2 of "COVID-19: a new deep learning computer-aided model for classification (v0.1)"

Abstract

Chest X-ray (CXR) image is one of the most feasible diagnosis modalities to early detect 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 paper, a model is proposed for analyzing and evaluating grayscale Chest X-Ray images called Chest X-Ray COVID Network (CXRVN) based 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, are applied, and the model has almost the same performance. Besides, CXRVN accepts CXR image in grayscale that is a perfect image representation for CXR and consumes 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 is evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score, and