Introduction

The novel coronavirus was first detected in December 2019 and spread around the globe rapidly. Now, it has affected almost every country with forty million confirmed cases and more than a million deaths on 18th October 2020 [1]. It has created a tremendous impact on healthcare facilities as well as an economic crisis. To prevent the spread of COVID-19, several national governments have introduced ‘lockdown’ to measure ‘social distancing’ and ‘isolation’ guidelines that limit the movement of people [2]. The coronavirus symptoms can range from cold to fever, as well as acute respiratory illness [3]. The infection of coronavirus is transmitted predominantly via droplets [4].

From the time of civilization, several diseases like heart disease [5], diabetes [6], liver disorder [7], breast cancer [8,9,10], COVID-19 [11,12,13], etc. caused severe and acute actions on human health, and artificial intelligence-based systems show better performance to identify those diseases. Fighting against COVID-19, modern technologies are playing significant roles in the development of a smart healthcare system [14, 15]. For example, a facial recognition system is used to trace the infected patients, and robots are used to deliver food and medicine in hospital and drones are applied to disinfect streets [16, 17]. Besides, the researchers around the globe are looking for emerging technologies to monitor and control this virus. Deep learning is such a technology that can be able to diagnose COVID-19 infected patients using radiological images and also used to discover new drugs and medicine so that it can recover infected patients and also utilized to produce a vaccine.

This paper focuses on the contributions of deep learning techniques to fight against the global pandemic. It provides a comprehensive review of deep learning applications that support the world healthcare system by reducing and suppressing the epidemic’s impact. The most recent applications are described throughout the study. The current challenges of existing systems with potential future directions are also outlined in this paper.

The remaining parts of the paper are arranged as follows. “Deep Learning Applications for COVID-19” described the most recent applications of deep learning techniques to combat ongoing pandemic in detail. The summary of the reviewed works is depicted in “Discussions”. In addition, the challenges of existing systems with possible future trends are demonstrated in “Discussions”. Lastly, “Conclusion” concludes the paper.

Deep Learning Applications for COVID-19

Deep learning is a subset of artificial intelligence that contains multiple layers to analyze data. In this model, data are filtered through several layers, where each successive layer using the output of the previous one to produce its output. The analysis of biomedical and healthcare problems helps medical professionals and researchers to find out the new scope for serving the healthcare communities. The detection of COVID-19 at an early stage and isolation of the affected people from others is the most crucial step in controlling this pandemic due to high transmissibility. The reverse polymerase chain reaction (RT-PCR) is considered as a key indicator [18] to diagnose COVID-19 cases; however, it is a time-consuming process with a high false-negative rate. Deep learning focuses on medical imaging, disease tracking, protein structure analysis, drug discovery, and virus severity and infectivity to combat coronavirus. Figure 1 shows several applications of deep learning for the COVID-19 pandemic. In recent studies, several works are found that used deep learning techniques to control COVID-19. The recent applications of deep learning are outlined as follows.

Fig. 1
figure 1

Deep learning applications for COVID-19 pandemic

Full size image

Medical Imaging for Diagnosis

With the rapid spread of COVID-19, there is growing interest in alternative methods for diagnosing coronavirus infection using medical imaging. Deep learning techniques have been used to process and analyze X-rays as well as computed tomography (CT) to help the doctor to predict COVID-19 infection [19, 20]. Several works are introduced, focusing on the detection of coronavirus using deep learning. Wang and Wong [33]. The ground-glass opacities are found in both lungs when the virus increases in the body. Drug repurposing is proposed to identify the patient’s illness that can be treated using existing medications.

Protein Structure Prediction

While entering the RNA genome into a cell, it combines with the host's protein production to duplicate RNA molecules by utilizing it. This is called "polymerase" that is used for a target in treatments [34]. Three-dimensional (3D) protein structure is determined by their genetically encoded amino acid sequence that impacts the function of the protein. Template modeling and template-free modeling are two approaches for the prediction task. For template sequence, template modeling predicts similar protein structures, and template-free modeling predicts unknown related structures [35]. Senior et al. [36] proposed an architecture called AlphaFold based on extended ResNet network [37] that used amino acid sequences and also extracted features using several sequences alignment from its to find out the distance and dispersal of angles between amino acid residues. This system is applied to predict several proteins structure related to COVID-19 [38]. Though these predictions still need to be verified experimentally, it would be helpful to perceive the functionality of coronavirus as well as medicine development for COVID-19.

Drug Discovery

In this COVID-19 pandemic, the crucial step is to identify the right drugs that can be committed for better treatment. There are some researches that are trying to discover effective drugs using deep learning architecture for COVID-19. Zhavoronkov et al. [39] utilized a pipeline to detect inhibitors for the 3C-like protease. The system used three types of information, such as crystal protein structure, co-crystallized ligands, and the homology model of the protein. For every case, several networks, including Generative Auto-encoders (GAs) and Generative Adversarial Networks (GANs), are used [

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  1. Department of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh

    Amanullah Asraf, Md. Zabirul Islam, Md. Rezwanul Haque & Md. Milon Islam

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K. N. and M. Shivakumar.

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Asraf, A., Islam, M.Z., Haque, M.R. et al. Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic. SN COMPUT. SCI. 1, 363 (2020). https://doi.org/10.1007/s42979-020-00383-w

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