Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques, Gonçalo; Agarwal, Deevyankar; Díez, Isabel de la Torre

doi:10.1016/j.asoc.2020.106691

Cited by 248 publications

(129 citation statements)

References 48 publications

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“…They utilized it for deep features extraction. On the other hand, in [49] the authors used Deep Recurrent Convolutional Neural Network (DRCNN) [50] as a predictor model for bankruptcy, while in [51] the authors built their own CNN structure model based on Efficient Net architecture with nine levels (nine layers) to classify X-ray images into three classes (Normal, Pneumonia, and COVID- 19). Table 34 shows the differences and the similarities between the proposed structure and the existing structures in literature.…”

Section: Discussionmentioning

confidence: 99%

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Alqudah

Alquran

et al. 2021

Applied Sciences

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Arabic and Hindi handwritten numeral detection and classification is one of the most popular fields in the automation research. It has many applications in different fields. Automatic detection and automatic classification of handwritten numerals have persistently received attention from researchers around the world due to the robotic revolution in the past decades. Therefore, many great efforts and contributions have been made to provide highly accurate detection and classification methodologies with high performance. In this paper, we propose a two-stage methodology for the detection and classification of Arabic and Hindi handwritten numerals. The classification was based on convolutional neural networks (CNNs). The first stage of the methodology is the detection of the input numeral to be either Arabic or Hindi. The second stage is to detect the input numeral according to the language it came from. The simulation results show very high performance; the recognition rate was close to 100%.

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Section: Discussionmentioning

confidence: 99%

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Alqudah

Alquran

et al. 2021

Applied Sciences

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“…Those coefficients have been added to the baseline network to improve the performance of the network and increase the classification accuracy and time complexity. There are B0 to B7 variants of this model with the resolution ranging from 224 to 600 [17] , [18] , [19] .…”

Section: Deep Learning Architecturesmentioning

confidence: 99%

Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification

Verma

Tripathi

Pant

2021

Materials Today: Proceedings

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COVID-19 is the present-day pandemic around the globe. WHO has estimated that approx 15% of the world's population may have been infected with coronavirus with a large number of population on the verge of being infected. It is quite difficult to break the virus chain since asymptomatic patients can result in the spreading of the infection apart from the seriously infected patients. COVID-19 has many similar symptoms to SARS-D however, the symptoms can worsen depending on the immunity power of the patients. It is necessary to be able to find the infected patients even with no symptoms to be able to break the spread of the chain. In this paper, the comparison table describes the accuracy of deep learning architectures by the implementation of different optimizers with different learning rates. In order to remove the overfitting issue, different learning rate has been experimented. Further in this paper, we have proposed the classification of the COVID-19 images using the ensemble of 2 layered Convolutional Neural Network with the Transfer learning method which consumed lesser time for classification and attained an accuracy of nearly 90.45%.

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“…Wieczorek et al [31] used neural networks by exploiting the NAdam training model to predict the spread of COVID-19 based on the real value data. Marques et al [32] proposed an automated medical diagnostic system by utilizing convolutional neural network with using Efficient Net architecture. Khan et al [33] applied an Auto-Regressive Integrated Moving Average (ARIMA) model on the realistic collected data to predict and forecast the affected cases of COVID-19 in future.…”

Section: Introductionmentioning

confidence: 99%

A stochastic numerical analysis based on hybrid NAR-RBFs networks nonlinear SITR model for novel COVID-19 dynamics

Shoaib

Raja

Sabir

et al. 2021

Computer Methods and Programs in Biomedicine

115

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Background: Mathematical modeling of vector-borne diseases and forecasting of epidemics outbreak are global challenges and big point of concern worldwide. The outbreaks depend on different social and demographic factors based on human mobility structured with the help of mathematical models for vector-borne disease transmission. In Dec 2019, an infectious disease is known as “coronavirus” (officially declared as COVID-19 by WHO) emerged in Wuhan (Capital city of Hubei, China) and spread quickly to all over the china with over 50,000 cases including more than 1000 death within a short period of one month. Multimodal modeling of robust dynamics system is a complex, challenging and fast growing area of the research. Objectives: The main objective of this proposed hybrid computing technique are as follows: The innovative design of the NAR-RBFs neural network paradigm is designed to construct the SITR epidemic differential equation (DE) model to ascertain the different features of the spread of COVID-19. The new set of transformations is introduced for nonlinear input to achieve with a higher level of accuracy, stability, and convergence analysis. Methods: Multimodal modeling of robust dynamics system is a complex, challenging and fast growing area of the research. In this research bimodal spread of COVID-19 is investigated with hybrid model based on nonlinear autoregressive with radial base function (NAR-RBFs) neural network for SITR model. Chaotic and stochastic data of the pandemic. A new class of transformation is presented for the system of ordinary differential equation (ODE) for fast convergence and improvement of desired accuracy level. The proposed transformations convert local optimum values to global values before implementation of bimodal paradigm. Results: This suggested NAR-RBFs model is investigated for the bi-module nature of SITR model with additional feature of fragility in modeling of stochastic variation ability for different cases and scenarios with constraints variation. Best agreement of the proposed bimodal paradigm with outstanding numerical solver is confirmed based on statistical results calculated from MSE, RMSE and MAPE with accuracy level based on mean square error up to 1E-25, which further validates the stability and consistence of bimodal proposed model. Conclusions: This computational technique is shown extraordinary results in terms of accuracy and convergence. The outcomes of this study will be useful in forecasting the progression of COVID-19, the influence of several deciding parameters overspread of COVID-19 and can help for planning, monitoring as well as preventing the spread of COVID-19.

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Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Cited by 248 publications

References 48 publications

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks

Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification

A stochastic numerical analysis based on hybrid NAR-RBFs networks nonlinear SITR model for novel COVID-19 dynamics

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