# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
## 1 Introduction  
The pandemic of COVID-19 is causing a genuine emergency at the moment [1] all over the world. With over 182,006,598 total COVID-19 cases around the world and 3,942,777 deaths already, as indicated by the World Health Organization (WHO) statistics [2], this pandemic poses the biggest medical danger towards mankind till date. Though the death rate is less than 2%, the highly contagious nature of COVID-19 is considered the main concern for the world population. Clinical studies reveal that a COVID-19 infected person may experience a dry cough, muscle pain, headache, fever, sore throat, and mild to moderate respiratory illness. The low accessibility of testing kits poses another serious problem in terms of the efficiency of its detection. At present, the tests for detecting the presence of COVID-19 are performed based on real-time reverse transcription-polymerase chain reaction (RT-PCR) [3]. RT-PCR detection of viral RNA from sputum or nasopharyngeal swabs requires specific hardware and also has relatively low sensitivity. It takes a minimum of 4-6 h to generate results. Another standard diagnosis of COVID-19 is currently the Nucleic Acid Amplification Testing (NAAT). However, NAAT is resource and time consuming and hence is not widely accessible. Ag-RDTs for SARS-CoV-2, usually in a Lateral Flow Immunoassay (LFI) cassette format, have recently been developed and commercialized [4]. These easy-to-use tests offer rapid case detection. However, its performance is highly variable based on the test characteristics and the population tested. Furthermore, the RDTs are sensitive only for the detection of patients with a high viral load and, hence, cannot be trusted as a major diagnostic method. This is why we choose CT scans in this paper, making our method more reliable. According to [5], chest X-rays and CT scans of COVID-19 suspected patients may help to diagnose COVID efficiently yet in a fast way using artificial intelligence (AI) techniques, wherein the AI model learns by itself to differentiate COVID CT scans from non-COVID CT scans after studying a set of images. However, the problem with chest X-rays is that they cannot differentiate soft tissues accurately [6] and, hence, cannot be fed into the AI models for an all-round evaluation. To overcome this, CT-scans can be used. Several works [7; 8] show considerable success in the application of AI and deep learning (DL) approaches for efficient detection of the disease from chest CT scans. DL-based approaches [9] accelerate classification by reducing the need for hand-designing features. Deep neural networks (DNN) use abstraction to represent data without any application-specific descriptors, thereby eliminating the manual step of feature engineering and representation required in conventional machine learning (ML) [10; 11]. Due to this, DL techniques have been used in computer science fields such as computer vision [12], speech recognition [13], and text processing [14]. In recent years, DL-based models have outperformed traditional statistical and ML-based strategies in most of the tasks. Computer vision and machine perception undoubtedly are some of the most influenced fields and have achieved great heights after the development of convolutional neural networks (CNN). The field of medical imaging has seen far-reaching effects due to recent progress in computer vision, for detection of diseases such as cancer [15], lung diseases [16] pneumonia [17; 18], seizures [19], MERS [20], SARS [21], drug discovery [22] and so forth. During the current COVID-19 pandemic, it has become even more important for such DL-based approaches to be used in real-time. DL-based models can potentially be of very high utility, especially when considering cost, speed, and ease. Many works have been done in the detection of COVID using DL [23; 24; 25; 26; 27]. In this era of big data and due to the availability of greater computation power, we can train these neural networks with ease. However, there are certain limitations to CNN models.