## 1 Introduction  
COVID-19, provoked by the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), has been spreading exponentially around the world since December 2019 starting from Wuhan, China, resulting in a global health crisis [1]. This highly infectious disease has been posing as the biggest current healthcare threat towards humanity, which has led the World Health Organization (WHO) to declare this outbreak to be a Public Health Emergency of International Concern (PHEIC), and it was recognized as a global pandemic [2, 3]. As of 5 April 2021, the WHO reported 131,309,792 worldwide cases with 2,854,276 deaths and a mortality rate exceeding 2% [4]. The typical clinical characteristics of COVID-19 cases range from asymptomatic to flu-like symptoms, fever, dry cough, tiredness, loss of taste and smell, to even a life threatening Acute Respiratory Distress Syndrome (ARDS) [5]. Up to this date, no effective treatment has yet been proven. Hence, accurate and rapid testing is extremely pivotal to lessen the spread of the virus. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is considered to be the gold standard method for confirming infected cases because it is able to identify SARS-CoV-2 RNA from respiratory specimens, obtained by nasopharyngeal or orophungal swabs, within 4 to 6 h [6]; however, the shortage of RT-PCR test kits is a major problem in many countries around the world. Furthermore, the sensitivity of RT-PCR screening is rather low as a result of high false-negative rates caused by several factors including sample preparation and quality control [7; 8; 9]; therefore, chest radiography imaging, X-ray (CXR), or computed tomography (CT scans), is usually used as a complementary examination in the rapid diagnosis and control of the coronavirus. Hence, the use of chest CT scans, as mentioned in this review [10], can counteract the limitations of the low sensitivity of RT-PCR tests, thus improving the accuracy and speed of diagnosis. Moreover, compared with the chest X-ray, CT scans are generally recommended thanks to their three-dimensionality and good visibility. There are various studies inspecting the imaging characteristics throughout the diagnosis, follow-up, and treatment of COVID-19 [11; 12]. It was found that patients presented chest radiographic abnormalities which exhibited similar features, including ground-glass opacities (GGO), an area of increased attenuation in the lung with preserved bronchial and vascular markings in the early stages; moreover, they presented pulmonary consolidation when the accumulation of fluid progresses to obscure bronchial and vascular regions in the latter stages. Consequently, accurate and rapid detection and localization of these tissue abnormalities is critical for early diagnosis and treatment of COVID-19. Recently, with the rapid development of artificial intelligence, and more specifically, deep learning technologies, Convolutional Neural Networks (CNNs) have been used a great deal in medical image processing thanks to their powerful feature representation and extraction. Several techniques based on CNNs have been published, showing promising performances in other disease diagnosing cases, such as cancer and so forth [13; 14], which should also have the same achievability in this novel pneumonia detection. In biomedical image analysis, the problems can be interpreted as classification and segmentation to identify and detect abnormal features and regions of interest (ROIs) via deep learning techniques, where the CNN and Unet based architectures are the most promising and popular choice among the research community. In this work, a deep learning-based diagnosis system was developed to automatically detect and analyze areas suspected to be infected with the COVID-19 virus from clinical CT images extracted from a publicly available chest CT scans dataset.