An ensemble deep transfer-learning approach to identify COVID-19 cases from chest X-ray images Taki Hasan Rafi Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka, Bangladesh takihasanrafi@gmail.com Abstract—Novel coronavirus began in Wuhan, China back in December 2019. It has now outspread all over the world. Around 23 million people are currently affected by the novel coronavirus. It causes around 800,000 deaths globally. There are just about 300,000 people contaminated by COVID-19 in Bangladesh too. As it is an exceptional new pandemic infection, its diagnosis is challenging for the medical community. In regular cases, it is hard for developing countries to test cases frequently. The RT - PCR test is a generally utilized analysis framework for COVID-19 case detection. However, by utilizing X-ray image- based programs, recognition can diminish the expense and testing time. So it is important to program an effective recognition system to identify positive cases. In this paper, the author proposes an ensemble deep learning model, combining two state-of-art pre-trained models as ResNet-152 and DenseNet-121 to identify COVID-19 cases. The experimental validation result is immensely well with an accuracy of 98.43% on the proposed model. The author also compares the ensemble model’s performance with ResNet-50 and DenseNet-121 separately. Index Terms—COVID-19, deep learning, ensemble, X-Ray, transfer learning. I. I NTRODUCTION A novel Coronavirus or COVID-19 is an infectious virus that has been transmitted through a set of all animals. Later, it has affected people too. Since December 2019, various examples of severe viral pneumonia identified in the seafood wholesale market in Wuhan City, China [2]. A Novel coro- navirus affected people severely was officially affirmed on January 6, 2020 [1]. As indicated by Nature, the spread of coronavirus (COVID-19) is getting relentless and has just arrived at the important epidemiological measures for it is to be announced as a pandemic [18]. COVID-19 is an intense settled disease however it has around 3% death rate globally [6]. Like other viral pneumonia, for example, a serious intense respiratory disorder can be caused by the coronavirus. So that COVID-19 can prompt intense respiratory serious condition [1] [2]. polymerase chain response (RT-PCR) [8]. COVID-19 can cause intense heart injury, in the vast majority of the cases, the patients who have co-morbidity like diabetes, circulatory strain, coronary illness [10]. The side effects of these sicknesses resemble ordinary influenza. The side effects can be discovered roughly in the middle of 14 days. As this COVID-19 is a new infection for the medical community, so explicit treatment for COVID-19 is still not viable. There are some recognized side effects in regards to COVID-19, declared by the World Health Orga- nization (WHO). For example, high fever or mellow fever, hack, breathing problem, exhaustion, muscle or body throbs, migraine, loss of taste or smell, sore throat, clog or runny nose, spewing, diarrhea. It straightforwardly influences the lung in some cases. X-Ray based images can assist us with knowing the lung condition so we can discover more COVID-19 cases as per the lung report. CT scan reports also can be utilized in the same purpose [13]. Around 15-20% of the patients fall into a serious medical condition, which means they require oxygenation as a major aspect of treatment [19]. There are several vaccines currently in phase-3, so it is hoping that the end of this year an effective vaccine can be found. While investigating images-based problems, Deep Convo- lutional Neural Network can comprehend more effectively by its state-of-art architecture. Deep neural-based frameworks can classify images or related problems more precisely and productively by its powerful algorithmic strength. To tackle the classification problem more accurately, the author combines two pre-trained ResNet-152 and DenseNet- 121 architectures to achieve the best possible result on the chest x-ray dataset. II. RELATED WORKS The novel coronavirus is a new virus in the medical commu- nity. Clinical researchers as well as deep learning specialists are attempting to detect COVID-19 cases. The fundamental objective is to distinguish COVID-19 cases in a less measure of time and minimal cost. So the AI researchers have handled this problem more efficiently by applying various state-of-art deep learning models. In this section, some related works have been presented. There is an essential need for a viable treatment for COVID- 19 affected people. The current spotlight has been on the improvement of novel therapeutics, including antivirals and antibodies, and developing vaccines. Gathering proof recom- mends that a subgroup of patients with serious COVID-19 may have a cytokine storm condition [7]. The most widely recognized test method for COVID-19 is the interpretation © IEEE 2020. This article is free to access and download, along with rights for full text and data mining, re-use and analysis