https://doi.org/10.1007/s10489-020-01978-9 Corona-Nidaan: lightweight deep convolutional neural network for chest X-Ray based COVID-19 infection detection Mainak Chakraborty 1 · Sunita Vikrant Dhavale 1 · Jitendra Ingole 2 Accepted: 25 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The coronavirus COVID-19 pandemic is today’s major public health crisis, we have faced since the Second World War. The pandemic is spreading around the globe like a wave, and according to the World Health Organization’s recent report, the number of confirmed cases and deaths are rising rapidly. COVID-19 pandemic has created severe social, economic, and political crises, which in turn will leave long-lasting scars. One of the countermeasures against controlling coronavirus outbreak is specific, accurate, reliable, and rapid detection technique to identify infected patients. The availability and affordability of RT-PCR kits remains a major bottleneck in many countries, while handling COVID-19 outbreak effectively. Recent findings indicate that chest radiography anomalies can characterize patients with COVID-19 infection. In this study, Corona-Nidaan, a lightweight deep convolutional neural network (DCNN), is proposed to detect COVID-19, Pneumonia, and Normal cases from chest X-ray image analysis; without any human intervention. We introduce a simple minority class oversampling method for dealing with imbalanced dataset problem. The impact of transfer learning with pre-trained CNNs on chest X-ray based COVID-19 infection detection is also investigated. Experimental analysis shows that Corona-Nidaan model outperforms prior works and other pre-trained CNN based models. The model achieved 95% accuracy for three-class classification with 94% precision and recall for COVID-19 cases. While studying the performance of various pre-trained models, it is also found that VGG19 outperforms other pre-trained CNN models by achieving 93% accuracy with 87% recall and 93% precision for COVID-19 infection detection. The model is evaluated by screening the COVID-19 infected Indian Patient chest X-ray dataset with good accuracy. Keywords Coronavirus · COVID-19 · SARS-CoV-2 · Chest X-Ray (CXR) · Radiology images · Deep learning 1 Introduction The outbreak of coronavirus occurred in December 2019, where China reported a cluster of unknown causes of pneumonia cases in the city of Wuhan, Hubei province to the World Health Organisation(WHO) [14, 30, 38]. This SARS- CoV-2 or COVID-19 disease spread rapidly around the world [30, 33] and considering severity, WHO announced COVID-19 as a pandemic. Till date (5 th June 2020), a This article belongs to the Topical Collection: Artificial Intelli- gence Applications for COVID-19, Detection, Control, Prediction, and Diagnosis  Mainak Chakraborty mainak.mail@gmail.com Extended author information available on the last page of the article. total of 6,675,011 cases of COVID-19 have been reported, including total 391,848 deaths worldwide [10]. Inhaling infected droplets may spread the disease, with an incubation period of between two and fourteen days [29]. People with cough, shortness of breath or difficulty breathing, fever, chills, muscle pain, loss of taste or smell, and sore throat symptoms may have COVID-19 [9, 29]. Other less common symptoms have been reported, such as nausea, vomiting, or diarrhea etc. [9]. Dr. Mike Ryan, Executive Director, WHO Health emergencies said, ”It is important to put this on the table: this virus may become just another endemic virus in our communities, and this virus may never go away” on 14 th May 2020 at the Geneva Virtual Press Conference [3]. WHO suggested that rapid testing is one of the effective measures to control the spread of SARS-CoV-2 infection [37]. Currently, Real-time reverse transcription- polymerase chain reaction (RT-PCR) testing technique is used for laboratory diagnosis of COVID-19 [6, 34]; however it suffers with following three issues: / Published online: 2 February 2021 Applied Intelligence (2021) 51:3026–3043