Role of deep learning in early detection of COVID-19: Scoping review

Alzubaidi, Mahmood; Zubaydi, Haider Dhia; Bin-Salem, Ali Abdulqader; Abd‐Alrazaq, Alaa; Ahmed, Arfan; Househ, Mowafa

doi:10.1016/j.cmpbup.2021.100025

Cited by 36 publications

(20 citation statements)

References 35 publications

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“…Most of the studies used deep learning to detect COVID-19 cases in early stage based on different diagnostic techniques. The most widely used techniques are convolutional neural network (CNN) and transfer learning (TL) [93]. The paper detailed that the use of AI in COVID-19 investigation can be summarized in terms of clinical image examination, drug design and pandemic prediction against coronavirus.…”

Section: Applying Deep Learning Algorithms In Healthcarementioning

confidence: 99%

A Review of Deep Learning Algorithms and Their Applications in Healthcare

et al. 2022

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Deep learning uses artificial neural networks to recognize patterns and learn from them to make decisions. Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior ability to learn from huge amounts of data. It was found that deep learning approaches can be used for big data analysis successfully. Applications include virtual assistants such as Alexa and Siri, facial recognition, personalization, natural language processing, autonomous cars, automatic handwriting generation, news aggregation, the colorization of black and white images, the addition of sound to silent films, pixel restoration, and deep dreaming. As a review, this paper aims to categorically cover several widely used deep learning algorithms along with their architectures and their practical applications: backpropagation, autoencoders, variational autoencoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsnets, transformer, embeddings from language models, bidirectional encoder representations from transformers, and attention in natural language processing. In addition, challenges of deep learning are also presented in this paper, such as AutoML-Zero, neural architecture search, evolutionary deep learning, and others. The pros and cons of these algorithms and their applications in healthcare are explored, alongside the future direction of this domain. This paper presents a review and a checkpoint to systemize the popular algorithms and to encourage further innovation regarding their applications. For new researchers in the field of deep learning, this review can help them to obtain many details about the advantages, disadvantages, applications, and working mechanisms of a number of deep learning algorithms. In addition, we introduce detailed information on how to apply several deep learning algorithms in healthcare, such as in relation to the COVID-19 pandemic. By presenting many challenges of deep learning in one section, we hope to increase awareness of these challenges, and how they can be dealt with. This could also motivate researchers to find solutions for these challenges.

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Section: Applying Deep Learning Algorithms In Healthcarementioning

confidence: 99%

A Review of Deep Learning Algorithms and Their Applications in Healthcare

et al. 2022

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“… [32] and Alzubaidi et al. [11] conducted reviews on the role of deep learning for the early detection of COVID-19. The reviews concluded that the datasets used for both training and test were imbalanced, principally due to absence of COVID-19 chest X-ray images.…”

Section: State-of-the-artmentioning

confidence: 99%

“…A deep understanding of internal organ structures and careful observation is a necessity or partially accurate diagnoses, poor treatment regimens and management of diseases, especially in developing countries, result as a consequence. Thus there is an unambiguous need for an accurate system supports the interpretation and analysis of chest X-ray images for early detection of COVID-19 [11] .…”

Section: Introductionmentioning

confidence: 99%

An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics

Ogechukwu¹,

Hindy²,

Ukwandu³

2022

Healthcare Analytics

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“…In computer science, machine learning (ML), deep learning (DL), artificial neural network (ANN) and reinforcement learning (RL) are subset techniques of AI that are used to perform different tasks on medical images such as classification, segmentation, object identification, and regression ( Fatima and Pasha, 2017 ; Kim et al., 2019b ; Shahid et al., 2019 ). Diagnosis using computer-aided detection (CAD) has moved toward becoming AI automated process in the medical images ( Castiglioni et al., 2021 ), which include most of the medical imaging data such as X-ray radiography, fluoroscopy, MRI, medical ultrasonography or ultrasound, endoscopy, elastography, tactile imaging, and thermography ( Alzubaidi et al., 2021a , 2021b ; Fujita, 2020 ). However, digitized medical images come with a plethora of new information, possibilities, and challenges.…”

Section: Introductionmentioning

confidence: 99%