# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 1 Introduction  
The world has been afflicted with a disastrous pandemic that has affected the population of our planet on a very large scale and which was named COVID-19 by the World Health Organization (WHO) in February 2020 [1]. The virus originated in Wuhan Cityin China and then spread throughout the entire globe with devastating effects, causing around 45 million deaths in a period of just 1.7 years, as per the World Health Organization report [2]. The number of victims of COVID-19 was about 0.22 billion across the world. Recovery from the illness can take as long as several weeks to months, with long-term effects causing organ damage [3; 4]. Moreover, the patient must be placed in isolation during that period to prevent the spread of COVID-19. This not only consumed lives, but also hindered everyday office work, social and family routines, and paralyzed the world economy [5; 6]. Researchers battled this disease since its emergence, looking for efficient and accurate detection methods, and uncovering solutions for its treatment, and vaccinations for human immunization [7; 8; 9]. Artificial intelligence geared with high-performance computers induced hope in medical science to diagnose diseases rapidly, which would otherwise take an enormous amount of time, if relying only on medical science and well-qualified and highly skilled doctors. In a study on breast cancer prediction, it was found that artificial intelligence could predict cancer at a rate higher than a well-organized evaluation of a panel of eleven pathologists. Hence, artificial intelligence would also help curtail medical costs incurred before treatment in hospitals and clinics [10; 11; 12; 13; 14; 15; 16; 17; 18]. Radiographic images, CT scans and chest X-rays show visual attributes exposing symptoms of COVID-19. The limited availability of expert radiologists, however, creates a bottleneck in the manual diagnosis of COVID-19. Therefore, the use of artificial intelligence should be, and already is, a viable source for the detection of COVID-19 using radiographic images [19; 20; 21]. This study aims to deploy a deep learning network and existing known models in convolutional neural networks, which are known to perform well on images, on a database of chest X-rays for COVID-19 detection to determine a viable architecture for developing a medical diagnosis system for accurate diagnosis of this virus. The study measured the performance gains of deep learning models on the largest COVID-19 chest X-ray image dataset, according to the best of the authors' knowledge [22]. Our research experimentation and analysis are different from previous research work in that they were performed on a larger database of chest X-ray images than used previously in the literature. The datasets utilized in the literature include far fewer COVID-19 chest X-ray images, ranging from 127 to 850 as per the authors reviewed in Section 2. This number is considerably lower compared with the number of samples available for the other cases; for example, in one study, the number of pneumonia cases exceeded 5000, while the number of COVID-19 cases was only 415 [23]. Here, a considerably low number of COVID-19 chest X-ray images and imbalance occurred. The study achieved the highest accuracy to date--as per the covered literature--in which 237 COVID-19, 1338 normal, and 1336 viral pneumonia cases were used; the authors specified that the number of COVID-19 cases was significantly lower and the problem was that of a highly imbalanced classification [24]. Moreover, in some cases in the literature, the authors balanced the classes by matching the number of samples of other classes to those of the COVID-19 samples, thereby achieving the maximum accuracy up to 99.90% using a very low number of chest X-ray test images; the total number of samples for COVID-19 chest X-rays for training and testing of only 404 [25].