ArticleBiomedical signal processing and control2021
Real‑time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm.
Article in Biomedical signal processing and control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 145 citations in OpenAlex.
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- Improved sports image classification using deep neural network and novel tuna swarm optimization.Scientific reports · 2024Article
- Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation.Scientific reports · 2024Article
- An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study.Heliyon · 2024Article
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- Multivariate time series short term forecasting using cumulative data of coronavirus.Evolving systems · 2023Article
- Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images.Expert systems with applications · 2023Article
- Fitness dependent optimizer with neural networks for COVID-19 patients.Computer methods and programs in biomedicine update · 2023Article
- A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms.Multimedia tools and applications · 2023Article
- Optimization design of two-layer Walker constellation for LEO navigation augmentation using a dynamic multi-objective differential evolutionary algorithm based on elite guidance.GPS solutions · 2023Article
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- Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision.Expert systems with applications · 2022Article
- COVID-19 diagnosis using chest CT scans and deep convolutional neural networks evolved by IP-based sine-cosine algorithm.Medical & biological engineering & computing · 2022Article
- Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- Chimp optimization algorithm in multilevel image thresholding and image clustering.Evolving systems · 2022Article
- Evolving deep convolutional neural networks by IP-based marine predator algorithm for COVID-19 diagnosis using chest CT scans.Journal of ambient intelligence and humanized computing · 2022Article
- Metaheuristics based COVID-19 detection using medical images: A review.Computers in biology and medicine · 2022Review
- A boosted chimp optimizer for numerical and engineering design optimization challenges.Engineering with computers · 2022Article
Corrections and comments
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Authors and funding
6 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep Learning (DL) methods fail to cover these aspects since training and fine-tuning the model's parameters consume much time. In this approach, the first phase comes to train a deep CNN working as a feature extractor, and the second phase comes to use Extreme Learning Machines (ELMs) for real-time detection. The main drawback of ELMs is to meet the need of a large number of hidden-layer nodes to gain a reliable and accurate detector in applying image processing since the detective performance remarkably depends on the setting of initial weights and biases. Therefore, this paper uses Chimp Optimization Algorithm (ChOA) to improve results and increase the reliability of the network while maintaining real-time capability. The designed detector is to be benchmarked on the
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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.