CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer

Gopatoti, Anandbabu; Vijayalakshmi, P.

doi:10.1016/j.bspc.2022.103860

Cited by 36 publications

(16 citation statements)

References 44 publications

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“…Model is simply trying to get down to the minimum error loss, node by node, until the final outcome with the maximum reduction of loss has reached. Weights are adjustable depending on the metric chosen [26].This method works well for data such as variable as Covid-19 data, but another crucial part is dividing the data for training.We need to have enough historical data for the model to train well which results in more accurate prediction or test trials.Part of the data is classified as training data and the rest is classified as testing, with a 70:30 training validation split.Here is where we need to look at some of the most relevant parameters of the model which are: learning rate, number of leaves, and the maximum depth. The loss function graph and the number of iterations are being observed when adjusting these parameters [27].…”

Section: Model Fitting and Resultsmentioning

confidence: 99%

“…For the data analysis, it is inevitable to check whether the data is seasonal or not and whether there is any autocorrelation, in order to get a better sense of choosing the parameters for the model [24].In [25][26][27][28][29] provided the segmentation of the CXR images in diagnosis of COVID-19 in Chest X-ray images. As seen in [Figure 3] the data was quite seasonal from October to July, and the it lowers, and it goes back up in the following October.…”

Section: Data Preprocessingmentioning

confidence: 99%

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A novel metaheuristic prediction approach for COVID-19 cases using XGBoost algorithm

Gopatoti¹

2023

IJSMIEN

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COVID-19 prediction is of great importance to build stronger government prevention and control of the global pandemic. This pandemic has a devastating impact on people and their lives. Many industries are suffering and are struggling to overcome unexpected pandemic challenges. Therefore, it is extremely important to develop up to date and practicable models for time series prediction of COVID-19 that would give promising results. In this paper, we build a model for predicting daily confirmed n w cases for the time series data of Europe by applying the Extreme Gradient Boosting (XGBoost) algorithm and using two performance parameters such as RMSE and MAPE to evaluate the effect of model fitting. Since machine learning methods (ML) have shown favorable results recently, among the tested models, XGBoost has shown to give the best results among the other models tested. Another advantage of the model is that we can use the XGBoost model to determine the robustness of the predictive model by adjusting parameter features. Our results are in line with the expectations of the performance of the model based on the available data and the variability of the data. By analyzing important features and updating the prediction for the new cases of the pandemic, the governments all around the globe could manage the situation much more effortlessly and with greater impact.

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Section: Model Fitting and Resultsmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

A novel metaheuristic prediction approach for COVID-19 cases using XGBoost algorithm

Gopatoti¹

2023

IJSMIEN

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“…We compared our proposed model to various methods, as shown in Table 12 . The comparison shows the proposed model, EVAE-Net, outperformed the methods in [ 37 , 40 , 41 , 95 , 96 ] using the same dataset (COVID-19 Radiography Database) for COVID-19 classification and methods that used other modalities [ 17 , 38 , 49 , 97 ]. It was worth noting that most of these methods only focused on either three classes or four classes.…”

Section: Results and Analysismentioning

confidence: 99%

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images

et al. 2022

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The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving 99.19% and 98.66% accuracy on four classes and three classes, respectively.

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“…The goal of this is to improve the image quality of CXR scans, as well as to detect and diagnose medical conditions at an affordable cost. In [16,17], authors implemented segmentation and classification based approaches to detect COVID-19 from the chest X-ray images. In today's world, methods using artificial intelligence such as machine learning [18] and deep learning [19] are beginning to show prospective outcomes in the analysis of medical pictures.…”

Section: Literature Surveymentioning

confidence: 99%

“…Figure 7 presents a graphical representation of the loss function for both the learning dataset and the validation dataset (a). In this example, the loss values for the learning dataset are obtained to be 0.051 and 0.044, In this context, SVM [17], KNN [15], and RF [18]are examples of current techniques. The effectiveness of the proposed methodology is compared to the efficacy of these existing approaches in Table 2.…”

Section: Subjective Evaluationmentioning

confidence: 99%

Deep learning framework for classification of COVID-19 from Chest X-rays

Gopatoti¹

2023

IJSMIEN

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The Coronavirus Disease 2019 (COVID-19) has emerged as the most pressing health concern in recent years. In addition, a wide variety of severe acute respiratory syndrome-related COVID-19 strains are now in the process of developing (SARS). Therefore, the identification of all of these variations utilising a Real-time polymerase chain reaction (RT-PCR) test is a challenging endeavour that takes a significant amount of time. Due of the intricate structure of the chest x-ray (CXR) picture, traditional approaches were unable to correctly categorise the COVID-19 at an early stage. As a result, the primary emphasis of this paper is placed on the use of an artificial intelligence strategy that is based on deep learning convolutional neural networks (DLCNN) for the classification of COVID-19 illness. In the beginning, the hybrid features are extracted from the CXR dataset by using stacked auto ensemble (SAE), a technique that retrieved features by partitioning data into various subsets. This approach was used. After that, a DLCNN model is trained with these characteristics in order to classify COVID-19 for each test CXR picture. According to the findings of the simulations, the suggested classification led to superior results in terms of both subjectivity and objectivity in comparison to both traditional machine learning and deep learning approaches.

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CXGNet: A tri-phase chest X-ray image classification for COVID-19 diagnosis using deep CNN with enhanced grey-wolf optimizer

Cited by 36 publications

References 44 publications

A novel metaheuristic prediction approach for COVID-19 cases using XGBoost algorithm

A novel metaheuristic prediction approach for COVID-19 cases using XGBoost algorithm

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images

Deep learning framework for classification of COVID-19 from Chest X-rays

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