COVID-19 X-rays Model Detection Using Convolution Neural Network

Ghaleb, Moshira S.; Ebied, Hala M.; Shedeed, Howida A.; Tolba, Mohamed F.

doi:10.1007/978-3-030-76346-6_1

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Wang et al [25] designed model COVID-Net considering chest X-ray images and this designed model achieved positive predictive value up to 98.9%. The authors of [51] proposed a model endowed with the principles of CNN to achieve an average of 96.8% in the detection of COVID-19. Jain et al [52] achieved 97.77% accuracy in COVID-19 detection by deep learning on chest X-ray images.…”

Section: Related Workmentioning

confidence: 99%

Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification

Ghosh

Banerjee²,

Das³

et al. 2022

Applied Sciences

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Accurate detection of an individual’s coronavirus disease 2019 (COVID-19) status has become critical as the COVID-19 pandemic has led to over 615 million cases and over 6.454 million deaths since its outbreak in 2019. Our proposed research work aims to present a deep convolutional neural network-based framework for the detection of COVID-19 status from chest X-ray and CT scan imaging data acquired from three benchmark imagery datasets. VGG-19, ResNet-50 and Inception-V3 models are employed in this research study to perform image classification. A variety of evaluation metrics including kappa statistic, Root-Mean-Square Error (RMSE), accuracy, True Positive Rate (TPR), False Positive Rate (FPR), Recall, precision, and F-measure are used to ensure adequate performance of the proposed framework. Our findings indicate that the Inception-V3 model has the best performance in terms of COVID-19 status detection.

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Section: Related Workmentioning

confidence: 99%

Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification

Ghosh

Banerjee²,

Das³

et al. 2022

Applied Sciences

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“…A growing number of studies have recently applied AI methods to solve meteorological issues. [5] used deep learning to extract the snow cover from remote sensing data. Using AI methods, [ 6] created two cloud recognition architectures that improved cloud recognition' s Issue 11 2023 14 precision.…”

Section: Introductionmentioning

confidence: 99%

Using Deep learning to Improve the Classification of Weather Phenomena from Images

mohamad¹

2023

آفاق آسيوية

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One of the most important phenomena at the moment is the identification of weather phenomena, which are crucial for numerous facets of daily life and are especially important for weather forecasts, transportation, tracking road conditions, farming, and forest management to protect of environment.Contrarily, few studies have attempted to classify images of actual weather events because it is challenging to do so using pictures, which are primarily reliant on human visual observations. This lessens the possibility of changing weather patterns. As far as we can determine, it takes time and is difficult to accurately distinguish between various weather events using traditional artificial vision. Even though some research has increased precision, also efficacy to recognizing phenomena employing artificial intelligence and even though AI approaches are better suited for categorization, they have discovered less different weather phenomena. This study suggests five artificial intelligence classification methods for meteorological events.In the meantime, we created a brand-The Weather Phenomenon Database (WEAPD), a brand-new dataset with 6,691 photos and 11 different types of weather phenomena, was just released. The categorization accuracy for AI methods in the WEAPD test group is about 84%. and the experimental results show how effective and better the models are for the specified AI State Information Service Issue 11 2023 12 Asian Perspectivestactics. Next studies on the classification of weather photographs and weather forecasting may use the creation of automatic, high-quality categorization of weather photos as a benchmark. The proportion is not higher than these values since the images are difficult to categorizes because they combine a variety of events.

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“…Recent studies have approved that CNN works effectively on feature extraction and classification phases. CNN has many layers with different filter sizes that extract the different image features and classify each image into the correct category [15,16]. Many CNN Models have been built and trained on more than a million images like AlexNet [17], ResNet [18], and VGGNet [19].…”

Section: Introductionmentioning

confidence: 99%

Weather Classification using Fusion Of Convolutional Neural Networks and Traditional Classification Methods

Ghaleb

Moushier

Shedeed

et al. 2022

IJICIS

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The weather phenomenon is very important in routine lives. The weather prediction, road electronic monitoring, traffic communication, capping inversion (CAP), afforestation, and the adjustment of the environmental issues are important factors to many decisions. Weather images classification may help in decision support systems. There are traditional and intelligent ways that can sufficiently achieve weather image classification. Traditional methods enhance the classification accuracy and the usability of weather phenomena. Researchers approve that machine learning has achieved better accuracies based on deep learning neural networks. This paper compares three different intelligent models by using a weather image dataset. The first model uses a convolution neural network (CNN) to classify five categories of weather images. The second model uses a fusion of convolution neural network and Decision Tree (DT). The third one uses a fusion of CNN and Support Vector Machine (SVM). The three models are applied to the collected dataset from Github and Kaggle. The study has achieved 92%, 93%, and 94% for CNN, CNN+DT, and CNN+SVM respectively. The Proposed methods have achieved high recognition accuracies for weather forecasting.

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COVID-19 X-rays Model Detection Using Convolution Neural Network

Cited by 8 publications

References 25 publications

Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification

Evaluation and Optimization of Biomedical Image-Based Deep Convolutional Neural Network Model for COVID-19 Status Classification

Using Deep learning to Improve the Classification of Weather Phenomena from Images

Weather Classification using Fusion Of Convolutional Neural Networks and Traditional Classification Methods

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