• Corpus ID: 239015848

Comparative Analysis of Deep Learning Algorithms for Classification of COVID-19 X-Ray Images

@article{Maheen2021ComparativeAO,
  title={Comparative Analysis of Deep Learning Algorithms for Classification of COVID-19 X-Ray Images},
  author={Unsa Maheen and Khawar Iqbal Malik and Gohar Ali},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.09294}
}
The Coronavirus was first emerged in December, in the city of China named Wuhan in 2019 and spread quickly all over the world. It has very harmful effects all over the global economy, education, social, daily living and general health of humans. To restrict the quick expansion of the disease initially, main difficulty is to explore the positive corona patients as quickly as possible. As there are no automatic tool kits accessible the requirement for supplementary diagnostic tools has risen up… 

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References

SHOWING 1-10 OF 25 REFERENCES
COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images
TLDR
This study demonstrates an AI-based structure to outperform the existing studies and shows how fine-tuned hyperparameters and augmented dataset make the proposed network perform much better than existing network designs and to obtain a higher COVID-19 diagnosis accuracy.
An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images
TLDR
An efficient diagnostic method that uses a combination of deep features and machine learning classification and implements an end-to-end diagnostic model that substantially advances the current radiology based methodology and can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.
Efficient-CovidNet: Deep Learning Based COVID-19 Detection From Chest X-Ray Images
TLDR
A Deep Learning approach to detect coronavirus from Chest X-Ray images is used and the EfficientNet Convolutional Neural Network (CNN) model is proposed, which achieves +2% accuracy, but it also attains higher sensitivity and Positive Predictive Values.
Automatic detection of COVID-19 from chest radiographs using deep learning
TLDR
This model is a non-contact process of determining whether a subject is infected or not and is achieved by using chest radiographs; one of the most widely used imaging technique for clinical diagnosis due to fast imaging and low cost.
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
TLDR
With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.
Deep learning approaches for COVID-19 detection based on chest X-ray images
TLDR
Results showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.
Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine
TLDR
The deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images and the method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people.
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
TLDR
COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.
Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images
TLDR
An open-source dataset of COVID-19 CXR-Dataset is presented, and a deep convolutional neural network model is introduced, which validates on 740 test images and achieves 87.3% accuracy, 89.67 % precision, and 84.46% recall.
Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection
TLDR
It is observed that weakly-labeled data augmentation improves performance with the baseline test data compared to non-augmented training by expanding the learned feature space to encompass variability in the unseen test distribution to enhance inter-class discrimination, reduce intra-class similarity and generalization error.
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