An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

Ahrabi, Sima Sarv; Scarpiniti, Michele; Baccarelli, Enzo; Momenzadeh, Alireza

doi:10.3390/computation9010003

Cited by 26 publications

(13 citation statements)

References 68 publications

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“…Recently, deep learning-based algorithms have been used by various researchers for combating the COVID-19 pandemic, including convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) for the COVID-19 detection, diagnosis, classification. Screening, drug repurposing, prediction, and forecasting ( Bogu and Snyder, 2021 , Desai et al, 2020 , Ghoshal and Tucker, 2020 , He et al, 2020 , Hu et al, 2020 , Khurana et al, 2021 , Baig et al, 2019 , Pan et al, 2021 , Sarv Ahrabi et al, 2021 , Sedik et al, 2021 , Soni and Roberts, 2021 ).…”

Section: Applications Of Ai To Combat Covid-19mentioning

confidence: 99%

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Khan

Mehran

Haq

et al. 2021

Expert Systems with Applications

152

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During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

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Section: Applications Of Ai To Combat Covid-19mentioning

confidence: 99%

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Khan

Mehran

Haq

et al. 2021

Expert Systems with Applications

152

View full text Add to dashboard Cite

show abstract

“…Another type of CNN's-special architecture design with optimized parameters was proposed in [17] that performs effectively on recent data. Where the focus of this architecture was the reducing complexities of a network while retaining a high level of accuracy.…”

Section: Related Workmentioning

confidence: 99%

Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

Saad¹,

Kamil²,

Alsayat³

et al. 2022

Computers, Materials &Amp; Continua

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has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients' data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy. Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results. In this paper, we used SqueezNet with a modified output layer to classify X-ray images into three groups: COVID-19, normal, and pneumonia. In this study, we propose a deep learning method with enhance the features of X-ray images collected from Kaggle, Figshare to distinguish between COVID-19, Normal, and Pneumonia infection. In this regard, several techniques were used on the selected image samples which are Unsharp filter, Histogram equal, and Complement image to produce another view of the dataset. The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type (COVID-19, Normal and Pneumonia). In the first scenario, the model has been tested without any enhancement on the datasets. It achieved an accuracy of 91%. But, in the second scenario, the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%. The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images. A comparison of the outcomes demonstrated the effectiveness of our DL method for classifying COVID-19 based on enhanced X-ray images.

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“…Although DL techniques have been applied with great success to identify cases of the NCP ( Sharma, 2020 , Shen, Wu, and Suk, 2017 , Zhou et al, 2017 ), there are nevertheless a number of challenges to be solved ( Chen et al, 2020 , Hammer et al, 2020 ). First of all, many solutions have been proposed in the literature, each of which has its own advantages and disadvantages, providing very different results: that is, there is no single solution to the problem ( Sarv Ahrabi et al, 2021 ). Furthermore, the DL architectures have a very large number of free parameters that must be adapted by the optimization algorithm and, in order to achieve the convergence, it is necessary to have a large amount of data, which is not always possible in practice ( Goodfellow et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

Scarpiniti

Ahrabi

Baccarelli

et al. 2022

Expert Systems with Applications

Self Cite

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Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising Convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.

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An Accuracy vs. Complexity Comparison of Deep Learning Architectures for the Detection of COVID-19 Disease

Cited by 26 publications

References 68 publications

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

Classification COVID-19 Based on Enhancement X-Ray Images and Low Complexity Model

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

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