Abstract
Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist doctors, researchers, pathologists, and other healthcare professionals. A decision support system (DSS) is an intelligent system that provides improved assistance in various stages of health-related disease diagnosis. At the same time, the SARS-CoV-2 infection that causes COVID-19 disease has spread globally from the beginning of 2020. Several research works reported that the imaging pattern based on computed tomography (CT) can be utilized to detect SARS-CoV-2. Earlier identification and detection of the diseases is essential to offer adequate treatment and avoid the severity of the disease. With this motivation, this study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification. The proposed EDLFM-SI technique aims to detect and classify the SARS-CoV-2 infection or not. Also, the EDLFM-SI technique comprises various processes, namely, data augmentation, preprocessing, feature extraction, and classification. Moreover, a fusion of capsule network (CapsNet) and MobileNet based feature extractors are employed. Besides, a water strider algorithm (WSA) is applied to fine-tune the hyperparameters involved in the DL models. Finally, a cascaded neural network (CNN) classifier is applied for detecting the existence of SARS-CoV-2. In order to showcase the improved performance of the EDLFM-SI technique, a wide range of simulations take place on the COVID-19 CT data set and the SARS-CoV-2 CT scan data set. The simulation outcomes highlighted the supremacy of the EDLFM-SI technique over the recent approaches.
Highlights
Intelligent decision support systems (IDSS) has become widely used in several applications of healthcare
The fusion-based feature extraction process is employed in which the fusion of MobileNet and capsule network (CapsNet) features is extracted
The features are fed into the cascaded neural network (CNN) model to allot the classes that exist in it. e perceptron linking that has been designed among the input and output has a procedure of direct relation, but FFNN linked generated among input and output was an indirect connection. e link was non-linear from shape with activation function from the hidden layer
Summary
Intelligent decision support systems (IDSS) has become widely used in several applications of healthcare. Chest CT screening was suggested, while the patient shows compatible symptoms with COVID-19; the outcome of its PCR tests is negative [4] It is necessary for an automatic detection tool that exploits the current developments in deep learning (DL) and artificial intelligence (AI), as well as the accessibility of CT images to construct AI-based tools to prevent further spreading and expedite the diagnoses method [5]. Is study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification for complex healthcare applications. Mohammed et al [15] presented an automatic CAD system for COVID19-based chest X-ray image analyses It is developed for COVID-19 diagnosis from another ARDS, MERS, and SARS infection. Alquzi et al [17] developed a result to detect persons with COVID-19 from CT images and ML models. is method is depending on a CNN model named EfficientNet
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