Deep neural networks for COVID-19 detection and diagnosis using images and acoustic-based techniques: a recent review

Abstract

The new coronavirus disease (COVID-19) has been declared a pandemic since March 2020 by the World Health Organization. It consists of an emerging viral infection with respiratory tropism that could develop atypical pneumonia. Experts emphasize the importance of early detection of those who have the COVID-19 virus. In this way, patients will be isolated from other people and the spread of the virus can be prevented. For this reason, it has become an area of interest to develop early diagnosis and detection methods to ensure a rapid treatment process and prevent the virus from spreading. Since the standard testing system is time-consuming and not available for everyone, alternative early screening techniques have become an urgent need. In this study, the approaches used in the detection of COVID-19 based on deep learning (DL) algorithms, which have been popular in recent years, have been comprehensively discussed. The advantages and disadvantages of different approaches used in literature are examined in detail. We further present the databases and major future challenges of DL-based COVID-19 detection. The computed tomography of the chest and X-ray images gives a rich representation of the patient’s lung that is less time-consuming and allows an efficient viral pneumonia detection using the DL algorithms. The first step is the preprocessing of these images to remove noise. Next, deep features are extracted using multiple types of deep models (pretrained models, generative models, generic neural networks, etc.). Finally, the classification is performed using the obtained features to decide whether the patient is infected by coronavirus or it is another lung disease. In this study, we also give a brief review of the latest applications of cough analysis to early screen the COVID-19 and human mobility estimation to limit its spread.

A Survey on harnessing the Applications of Mobile Computing in Healthcare during the COVID-19 Pandemic: Challenges and Solutions

“…Therefore, it must be filtered before adding it to the dataset [79] . However, some data mining and machine learning techniques can be effectively applied to address these challenges [80] .…”

Explaining Deep Learning Hidden Neuron Activations using Concept Induction

“…11 neurons -neuron number4,9,11,12,15,16,23,27, 60, 62, and 63 got activated by more than 90% activations in all the three cases.-10 neurons -neuron numbers 6, 13, 29, 36, 37, 39, 45, 52, 54, 59 were below 1% activations in all three cases. the rest activations are in the range of 1 -56.52%.…”

“…4 In continuation to the mentioned observation, the output of a network's classification can be altered by introducing Adversarial examples [6], and there are many more attack techniques. It becomes a need to understand the reasoning behind how a system behaves and generates an output in a human-interpretable way, especially since the popularity of these systems has grown to such an extent that these systems are responsible for decisions previously taken by human beings in safety-critical situations, for example like self-driving cars [8], drug discovery and treatment recommendations [27,12].…”

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

“…Patients can quickly get CXR images in their homes or quarantine facilities since CXR facilities are available even in the most distant locales. Recently, CXR images based on Artificial Intelligence (AI) have been utilized widely to detect COVID-19 instances [3,4]. Given the fast spread of COVID-19, however, such testing might reduce the effectiveness of pandemic prevention and control.…”

“…Since the outbreak of COVID-19, the majority of state-ofthe-art methods have primarily focused on using transfer learning techniques to implement their systems as shown in the recent survey published in [3]. One of the main challenges in using transfer learning techniques for detecting COVID-19 is the amount of time it takes to apply these methods to large datasets, especially when dealing with CXR images.…”