ArticleComputers in biology and medicine2021
A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19).
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 32 citations in OpenAlex.
- Article
- Article
- A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.Soft computing · 2023Article
- Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution.Journal of digital imaging · 2023Article
- X-ray image based COVID-19 detection using evolutionary deep learning approach.Expert systems with applications · 2022Article
- A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest.Informatics in medicine unlocked · 2022Article
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has had a detrimental impact on normal activities, public safety, and the global financial system. To identify the presence of this disease within communities and to commence the management of infected patients early, positive cases should be diagnosed as quickly as possible. New results from X-ray imaging indicate that images provide key information about COVID-19. Advanced deep-learning (DL) models can be applied to X-ray radiological images to accurately diagnose this disease and to mitigate the effects of a shortage of skilled medical personnel in rural areas. However, the performance of DL models strongly depends on the methodology used to design their architectures. Therefore, deep neuroevolution (DNE) techniques are introduced to automatically design DL architectures accurately. In this paper, a new paradigm is proposed for the automated diagnosis of COVID-19 from chest X-ray images using a novel two-stage improved DNE Algorithm. The proposed DNE framework is evaluated on a real-world dataset and the results demonstrate that it provides the highest classification performance in terms of different evaluation metrics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.