Efficient multimodal deep-learning-based COVID-19 diagnostic system for noisy and corrupted images.
J King Saud Univ Sci
; 34(3): 101898, 2022 Apr.
Article
en En
| MEDLINE
| ID: mdl-35185304
ABSTRACT
INTRODUCTION:
In humanity's ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination.OBJECTIVES:
Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs.METHODS:
This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers.RESULTS:
In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images.CONCLUSIONS:
Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated.
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Base de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
J King Saud Univ Sci
Año:
2022
Tipo del documento:
Article