Comparative analysis of deep learning models for COVID-19 detection

Kumari, Shweta; Ranjith, E.; Gujjar, Abhishek; Narasimman, Siranjeevi; Zeelani, H S Aadil Sha

doi:10.1016/j.gltp.2021.08.030

Cited by 18 publications

(12 citation statements)

References 10 publications

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“…Diagnosis, techniques such as Covid-Net CNN, ConoNet CNN, Bayes SqueezeNet, and CoroNet AutoEncoders, have performed superiorly with high accuracies. Other deep learning algorithms have diagnosed COVID-19 after being used on CT-scan datasets, such as the WOA-CNN, CRNet, and CNNs [88].…”

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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“…DL includes both supervised and unsupervised architecture Saleem and Chishti (2021a) including there are several algorithms used in several problem solving implementations presented in this review article such as Convolutional Neural Network (CNN) (Kumari et al 2021;Hao et al 2021;Dimililer et al 2021a;Saha et al 2021;Singaravel et al 2019;Bai et al 2020;Tong et al 2020;Hong et al 2020;Sharma and Mir 2020;Lei et al 2020;Maleki et al 2020;Gomez-Fernandez et al 2020;Iqbal and Qureshi 2020;Peddireddy et al 2020;Neb et al 2020;Yamaguchi et al 2019;Tarabishy et al 2020;Gomez-Donoso et al 2017;Qi et al 2017;Qsi et al 2016;Su et al 2015;Maturana and Scherer 2015;Ji et al 2013;Z. Zhang et al 2018;Kumari et al 2021;Z. Zhang et al 2018), Recurrent Neural Network (RNN) (Saha et al 2021;Bai et al 2020;Hong et al 2020;Iqbal and Qureshi 2020;Alshehri et al 2020;Qi et al 2017), Auto-Encoder (AE) (Hao et al 2021;Bai et al 2020;Tong et al 2020;Hong et al 2020;Iqbal and Qureshi 2020;Alshehri et al 2020), Generative Adversarial Network (GAN) …”

Section: Literature Review Deep Learning (Dl)mentioning

confidence: 99%

“…CNN is often used in learning applications to process image-based data because CNN is designed with several convolutional and pooling layers that are all interconnected (Fig. 7) so that the CNN feature extraction mechanism can automatically find valuable information in detail from each image pixel that has been processed trained Kumari et al (2021). This CNN layer consists of many units where the units are arranged in a plane that captures the sample image to be trained (Fig.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

How do we choose the appropriate deep learning method for our case? A systematic literature review

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et al. 2022

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In the era of technology 4.0, there are many problems in multiple sectors of life that are difficult for humans to solve, ranging from issues in the education quality performance system, difficulties in disease diagnosis, problems in manufacturing systems, construction, food grading, quality control, Etc. Various efforts have been made to solve these problems, from the conventional method of manually retrieving data to obtain the best solution to using a big data-based approach with deep learning. Deep learning has successfully solved problems in various sectors, proving that using big data on deep learning algorithms gives significant results. This systematic review aims to review the studies that have been carried out on applying deep learning to solve or help problems in various sectors. This systematic review shows an overview of deep learning neural networks created in the completion process, the differences in the artificial intelligent methods used, and the advantages and disadvantages of deep learning in various models. It identifies challenges and recommendations for the future. The methods used in this systematic review include search strategies, selecting literature studies, and managing and extracting data. Based on the systematic review results, we know that Convolutional Neural Network (CNN) is the most widely used model for this deep learning algorithm to recognize the feature, along with the image-based data transformation strategy. Finally, deep learning has become very popular because it can transform various data types to get the desired result.

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“…Kumari et al used four convolutional neural networks, InceptionV3, VGG16, Xception and ResNet50 in 2021, using a total of 2000 chest X-ray images and 2000 CT images. The results show that the binary classification accuracy of VGG16 in detecting COVID-19 and non-COVID-19 in chest X-ray images was 98.00%; the binary classification accuracy of Xception in detecting COVID-19 and non-COVID-19 in CT images was 83.00% ( 22 ). Ahsan et al fine-tuned eight convolutional neural networks including VGG16, VGG19, ResNet15V2, InceptionResNetV2, ResNet50, DenseNet201, MobilenetV2, and NasNetMobile in 2020, using a total of 400 chest X-ray images and 400 CT images.…”

Section: Introductionmentioning

confidence: 99%