ArticleApplied soft computing2021
An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Innovative applications of artificial intelligence during the COVID-19 pandemic.Infectious medicine · 2024Review
- A comprehensive review of COVID-19 detection with machine learning and deep learning techniques.Health and technology · 2023Article
- DBiomedical signal processing and control · 2023Article
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images.Multimedia tools and applications · 2023Article
- Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.Applied soft computing · 2023Article
- Impact of quarantine on fractional order dynamical model of Covid-19.Computers in biology and medicine · 2022Article
- A deep fuzzy model for diagnosis of COVID-19 from CT images.Applied soft computing · 2022Article
- Leveraging Tweets for Artificial Intelligence Driven Sentiment Analysis on the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2022Article
- BEMD-3DCNN-based method for COVID-19 detection.Computers in biology and medicine · 2022Article
- Central hubs prediction for bio networks by directed hypergraph - GA with validation to COVID-19 PPI.Pattern recognition letters · 2022Article
- Article
- COVID-Transformer: Interpretable COVID-19 Detection Using Vision Transformer for Healthcare.International journal of environmental research and public health · 2021Article
- SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19.Biocybernetics and biomedical engineeringArticle
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Authors and funding
7 authors.
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Abstract
In recent times, COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, pre-processing is applied to enhance the quality of the image. Next, the CRNN model is used for feature extraction, followed by hyperparameter tuning of CRNN via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using the BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model, and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.
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