ArticleBiocybernetics and biomedical engineering
WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images.
Article in Biocybernetics and biomedical engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 25 citations in OpenAlex.
- Wasserstein Deep Convolutional GAN With Growth Optimizer for Multi-Modal Feature Extraction in Cardiovascular Diagnosis.Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions · 2026Article
- Analysis of hybrid CNN models optimized with metaheuristic algorithms for melanoma detection.Scientific reports · 2026Article
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning.Scientific reports · 2025Article
- Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.Narra J · 2025Review
- Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning.Biomimetics (Basel, Switzerland) · 2023Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.Biocybernetics and biomedical engineering · 2023Article
- Jaya-tunicate swarm algorithm based generative adversarial network for COVID-19 prediction with chest computed tomography images.Concurrency and computation : practice & experience · 2022Article
- COVID-19 detection on chest X-ray images using Homomorphic Transformation and VGG inspired deep convolutional neural network.Biocybernetics and biomedical engineeringArticle
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists' burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.
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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 Socratic graph.