Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review

Abstract

Covid-19 is now one of the most incredibly intense and severe illnesses of the twentieth century. Covid-19 has already endangered the lives of millions of people worldwide due to its acute pulmonary effects. Image-based diagnostic techniques like X-ray, CT, and ultrasound are commonly employed to get a quick and reliable clinical condition. Covid-19 identification out of such clinical scans is exceedingly time-consuming, labor-intensive, and susceptible to silly intervention. As a result, radiography imaging approaches using Deep Learning (DL) are consistently employed to achieve great results. Various artificial intelligence-based systems have been developed for the early prediction of coronavirus using radiography pictures. Specific DL methods such as CNN and RNN noticeably extract extremely critical characteristics, primarily in diagnostic imaging. Recent coronavirus studies have used these techniques to utilize radiography image scans significantly. The disease, as well as the present pandemic, was studied using public and private data. A total of 64 pre-trained and custom DL models concerning imaging modality as taxonomies are selected from the studied articles. The constraints relevant to DL-based techniques are the sample selection, network architecture, training with minimal annotated database, and security issues. This includes evaluating causal agents, pathophysiology, immunological reactions, and epidemiological illness. DL-based Covid-19 detection systems are the key focus of this review article. Covid-19 work is intended to be accelerated as a result of this study.

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

“…However, researchers can continuously explore the potential of DL methods such as Attention-based LSTM [ 45 , 47 ], end-to-end models [ 134 ], and Transformer models [ 187 ] and try advanced recognition algorithms to improve the performance of IST for medical applications. Moreover, the fusion of voice signals with signals of other modalities such as electroacoustic gate signals, EMR, X-ray images, and ultrasound [ 4 , 5 ] will be more valuable for disease diagnosis in smart hospitals. For example, combining the chest X-ray images and cough sounds-based COVID-19 non-contact classification methods will minimize severity and mortality rates during the pandemic [ 5 , 6 , 188 , 189 ].…”

“…With the development of robotics and artificial intelligence (AI) technologies, machines can achieve more efficient and accurate disease diagnosis and assessment in some cases and replace nurses to assist patients in their lives, which alleviate the problem of insufficient medical resources. For example, intelligent image processing methods based on deep learning (DL) have been applied to processing X-ray, CT, ultrasound, and facial images for diagnosing diseases such as COVID-19 detection [ [4] , [5] , [6] ], paralysis assessment [ 7 , 8 ], and autism screening [ 9 ]. In addition, intelligent speech technology (IST) plays a critical role in smart hospitals because language is the most natural mean of communication between doctors and patients and contains much information, such as patients’ identity, age, emotion, and even symptoms of diseases [ 10 ].…”

A Radial Basis Function Neural Network for Stochastic Frontier Analyses of General Multivariate Production and Cost Functions

“…Neural networks (NN) are widely used for function approximation, density estimation, kernel regression, classification [ 5 ], data generation [ 6 ], deep learning [ 7 ] and interpolation problems [ 8 ]. The NN literature has matured over the last three decades and is also continuously growing [ 9 ].…”

MGDGAN: Multiple Generator and Discriminator Generative Adversarial Networks for Solving Stochastic Partial Differential Equations

“…Neural networks (NNs) have shown the potential to overcome the curse of dimensionality [16]. NN models have made breakthroughs in many applications such as object detection [3], Covid-19 diagnosis [7], speech recognition [11], natural language processing [12], image generation [17], recommendation system [18], cosmological simulation [29], robot systems [34], and anomaly detection [35]. Many attempts have been made to solve deterministic partial differential equations (PDEs) by using deep neural networks (DNNs) [6], [9], [13], [22], [23], [24], [32], [36], [47], [50].…”