Name
Title | ||
|---|---|---|
Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities |
Abstract | ||
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AbstractCOVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3453168 | ACM Transactions on Internet Technology |
Keywords | DocType | Volume |
COVID-19, classification, deep neural network, deep learning, pre-processing | Journal | 22 |
Issue | ISSN | Citations |
3 | 1533-5399 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Shankar | 1 | 0 | 0.34 |
| Eswaran Perumal | 2 | 0 | 1.01 |
| Mohamed Elhoseny | 3 | 0 | 0.34 |
| Fatma Taher | 4 | 0 | 0.34 |
| B. B. Gupta | 5 | 518 | 46.49 |
| Ahmed A. Abd El-Latif | 6 | 0 | 0.34 |