Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review

Swapnarekha, H.; Behera, Himansu Sekhar; Nayak, Janmenjoy; Naik, Bighnaraj

doi:10.1016/j.chaos.2020.109947

Cited by 162 publications

(85 citation statements)

References 98 publications

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“…Similarly, it may be an excellent proposition to aid prognosis and evaluation of recuperating/follow-up patients, using AI-based rapid and less time-consuming tools. Recently, researchers have made attempts to develop chest X-ray image-based COVID-19 classification or identification methods [ 36 ], with different capabilities. However, the studies possess some significant limitations that need to be resolved to develop more reliable and accurate classification models.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

Sharma

Rani

Gupta

2020

International Journal of Biomedical Imaging

103

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The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

Sharma

Rani

Gupta

2020

International Journal of Biomedical Imaging

103

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“…The SARS-Cov-2 pandemic has introduced an evident research boom into biophysical and mathematical modeling of infection expansions. Main efforts have been related to identification of factors [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] that help prognosticate the pandemic development what is a clear concern in the atmosphere of anxiety and social limitations. Among a variety of infection expansion model types, the SIR- (susceptible-infectives-recovered, where infectives are indistinguishable from ill) models occupy a particular place.…”

Section: Introductionmentioning

confidence: 99%

Analytical features of the SIR model and their applications to COVID-19

Kudryashov

Chmykhov

Vigdorowitsch

2021

Applied Mathematical Modelling

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Highlights A new approach to analysis of the infection expansion based on first integrals of the mathematical model; PainlevȨ analysis of the classic two-parameter SIR-model; The new general analytical solution of the model, structurally featured through its clear relation to epidemiological data; Re-thinking the classic SIR model with application to SARS-Cov-2 case

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“…They recommend the need to test the models globally for better global forecasting. Swapnarekha et al [3] presented a state-of-the-art analysis regarding use of machine learning and deep learning (DL) methods in the diagnosis and prediction of Covid -19 for its effective control. The analysis is done based on the journals by country and performance analysis of statistical, ML and DL approaches.…”

Section: Related Workmentioning

confidence: 99%

Realtime forecasting of COVID 19 cases in Karnataka state using Artificial neural network (ANN)

Shetty

2020

Preprint

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COVID-19 is a pandemic that has caused lot of deaths and infections in the last 6 months and is showing an increasing trend not only in the number of infections and deaths, but also in the recovery rate. Accurate prediction models are very much essential to make proper forecasts and take necessary actions. This study demonstrates the capability of Multilayer Perceptron (MLP, an ANN model for forecasting the number of infected cases in the state of Karnataka in India. It is trained using a fast training algorithm namely, Extreme Learning machine (ELM) to reduce the training time required. The parameters required for the forecasting model have been selected using partial autocorrelation function (PACF), which is a conventional method and its performance has been compared with parameters selected using cuckoo search (CS) algorithm, which is a very popular nature-inspired optimization algorithm. The testing of the forecasting model has been done and comparison between the two parameter selection methods has been carried out. Use of CS algorithm has resulted in a better forecasting performance based on mean absolute percentage error (MAPE), with a value of 6.62 % on training data and 7.03% on the test data. Further to check the efficacy of the model, the data of COVID-19 cases of Hungary from 4th March to 19th April 2020 has been used, which resulted in a MAPE of 1.55%, thereby establishing the robustness of the proposed ANN model for forecasting COVID-19 cases for the state of Karnataka.

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Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review

Cited by 162 publications

References 98 publications

Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

Analytical features of the SIR model and their applications to COVID-19

Realtime forecasting of COVID 19 cases in Karnataka state using Artificial neural network (ANN)

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