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- W3133930341 abstract "The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field." @default.
- W3133930341 created "2021-03-15" @default.
- W3133930341 creator A5001551009 @default.
- W3133930341 creator A5009651148 @default.
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- W3133930341 creator A5076294004 @default.
- W3133930341 creator A5078715775 @default.
- W3133930341 creator A5091310474 @default.
- W3133930341 date "2021-03-02" @default.
- W3133930341 modified "2023-10-02" @default.
- W3133930341 title "Deep Learning–Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis" @default.
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