Synergic Deep Learning For Smart Health Diagnosis Of Covid-19 For Connected Living And Smart Cities
Source of Publication
Acm Transactions On Internet Technology
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.
Association for Computing Machinery (ACM)
COVID-19, classification, deep neural network, deep learning, pre-processing
Shankar, K.; Perumal, Eswaran; Elhoseny, Mohamed; Taher, Fatma; Gupta, B. B.; and Abd El-Latif, Ahmed A., "Synergic Deep Learning For Smart Health Diagnosis Of Covid-19 For Connected Living And Smart Cities" (2022). All Works. 5335.
Indexed in Scopus