Since the beginning of COVID-19, scientists all over the world have been working on four aspects of this infectious virus: diagnosis, vaccination, treatment, and intervention against disease spread. While scientists in the medical field have been tackling these issues from a purely chemical and medical aspect, computer scientists have been working on diagnosis using machine learning techniques. Many research articles have also been written on the subject of the analysis of the spread of the disease and predictions of the spread of the disease using machine learning methods. The first requirement for using machine learning in COVID-19 diagnosis is the development of datasets. In [
8], the authors developed a dataset from various websites and publications to obtain 123 frontal view X-rays. In [
9], the authors developed a homogeneous dataset they named COVIDGR-1.0, containing a balanced number of X-rays for all levels of severity of COVID-19 cases. COVIDGR-1.0 is composed of 426 negative cases and 426 positive cases for a total of 852 images. Other datasets used in these studies are available in [
10,
11,
12,
13]. Other research involving machine learning tackled other aspects of COVID-19 such as a tweet dataset [
14] and face mask detection [
15].
In this paper, we primarily focus on the diagnosis of COVID-19 using X-ray images; thus, the remainder of the literature review highlights research conducted on the diagnosis of COVID-19. In [
16], the authors proposed the use of transfer learning with deep learning methods to detect COVID-19 using X-ray, ultrasound, and computerized tomography (CT) scans. They applied a VGG-19 model with specific parameters. They further proposed a preprocessing phase to minimize unwanted noise in the images. They concluded that ultrasound images were better for detection than CT scans and X-rays. They reported a precision of 100% for ultrasound, 84% for CT scans, and 86% for X-rays. They used the National Institute of Health chest X-ray dataset, the dataset specified in [
17], and the COVID-CT dataset. In [
18], the authors proposed several approaches for the detection of COVID-19 using chest X-rays. These included the fine-tuning of pretrained convolutional neural networks (CNNs), deep feature extraction, and end-to-end training of a developed model. They used ResNet 18, VGG19, VGG16, ResNet101, and ResNet50 for deep feature extraction and fine-tuning. For the classification phase, they used support vector machines (SVMs) with various kernel functions to include quadratic, linear, Gaussian, and cubic functions. They also proposed a CNN method and used a dataset of 200 healthy images and 180 COVID images. They reported an average accuracy of 94.7%. In [
19], the authors proposed deep learning methods for the detection of COVID-19 using chest X-ray images. They proposed the use of a deep CNN network for the detection of COVID-19 on a dataset consisting of 1000 images that they produced relying on an original dataset and augmentation. They reported an F-measure range between 95% and 99%. They reported accuracy of 88.43% and 94.8% for Jordan and Australia, respectively. In [
20], the authors proposed the use of X-rays for the initial diagnosis of COVID-19 patients selected for further reverse transcription polymerase chain reaction (RT-PCR) testing. They proposed a system called CovidAID using deep neural networks using a publicly available dataset [
8]. They used CheXNet, which consists of 121 layers of dense convolutional networks (DenseNet). They tested the system for three classes and four classes, reporting accuracies of 90.5% and 87.2%, respectively. In [
21], the authors proposed a multitask deep learning algorithm used on both X-rays and CT scans for the diagnosis of COVID-19. They used the dataset in [
7] combined with the dataset in [
8]. They propose the use of an inception residual recurrent CNN with transfer learning (TL) for the detection of COVID-19 and then the use of an NABLA-N model for segmenting the infected region. They reported a testing accuracy of 98.78% on CT scans and 84.67% on X-ray images. In [
22], the authors proposed the use of CNN for COVID-19 detection and achieved an accuracy of 85%. This was achieved using the dataset collected by the authors from three publicly available datasets: (1) IEEE COVID Chest X-ray Dataset (2) COVID-19 Chest X-ray Dataset Initiative, and (3) COVID-19 Radiography Database. However, the authors argued that synthetic datasets can add to the accuracy of CNN models; thus, they proposed the use of an auxiliary classifier generative adversarial network (ACGAN) to generate synthetic chest X-rays. Using the dataset with the added synthetic images, they reported that the CNN accuracy increased to 95%. In [
23], the authors proposed a novel CNN model consisting of 22 layers, called CoroDet, for the automatic diagnosis of COVID-19 using chest X-rays and CT scans. Their proposed system was designed for binary classification and multiclass classification, i.e., three-class (pneumonia, normal, and COVID-19) and four-class (bacterial pneumonia, viral pneumonia, normal, and COVID-19) classification. They reported classification accuracies of 99.1%, 91.2%, and 94.2% for binary, four-class, and three-class classification, respectively. They also compiled their dataset from publicly available datasets, which they claimed to be the largest dataset as of writing the paper. In [
24], the authors took the poster anterior (PA) view of chest X-ray images of COVID-19 infected patients and healthy patients. They compared the performance of three deep learning methods in the detection of COVID-19, namely, Inception V3, Xception, and ResNeXt. They collected a dataset consisting of 6432 images from Kaggle and used 5467 images for training and 965 for validation. They obtained the highest accuracy using Xception with an accuracy of 97.97%. In [
25], the authors proposed the use of advanced deep learning models along with transfer learning strategies utilizing a custom input size for each deep learning model to achieve the best performance. They used CT image datasets, namely, COVID19-CT and SARS-CoV-2 CT-Scan, reporting average accuracies of 92.9% and 99.4%, respectively. They further used visualization techniques to segment and get an accurate localization of the COVID-19-infected regions. In [
26], the authors proposed a machine vision method for the detection of COVID-19 using chest X-rays. They proposed that the features be extracted using CNN and histogram-oriented gradient (HOG). They employed the use of a modified anisotropic diffusion filter method (MADF) to preserve edges and reduce noise. They also used watershed segmentation to segment the region of the image that showed COVID-19 infection. The classification was achieved through a CNN using VGGNet. They used three publicly available datasets in their study. They reported an accuracy of 99.49% for binary classification (infected with COVID-19 or healthy). In [
27], the authors proposed an optimized deep learning method for the automatic diagnosis and classification of COVID-19 using X-ray images. They used transfer learning algorithms such as DenseNet, VGG16, VGG19, GoogleNet, and AlexNet. They reported the highest accuracy of 95.63% using VGG16.