Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities

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題名:	Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities
作者:	SHANKAR, K.;SHANKAR, K.;PERU, ESWARAN;ESWARAN PERUMAL;ELHO, MOHAMED;ELHOSENY, MOHAMED;TAHER, FATMA;TAHER, FATMA;Bhoosha, Brij;Gupta, Brij Bhooshan;ABD, AHMED A.;AHMED A. ABD EL-LATIF
貢獻者:	資訊電機學院資訊工程學系
日期:	2022-08-01
上傳時間:	2023-03-29 02:50:26 (UTC+0)
出版者:	亞洲大學
摘要:	COVID-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.
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