ArticleJournal of biomolecular structure & dynamics2022
ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images.
Article in Journal of biomolecular structure & dynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 88 citations in OpenAlex.
- Enhanced COVID-19 Detection from X-ray Images with Convolutional Neural Network and Transfer Learning.Journal of imaging · 2024Article
- HQDCNet: Hybrid Quantum Dilated Convolution Neural Network for detecting covid-19 in the context of Big Data Analytics.Multimedia tools and applications · 2023Article
- A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information.Infection and drug resistance · 2023Article
- COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network.Health information science and systems · 2022Article
- The Capacity of Artificial Intelligence in COVID-19 Response: A Review in Context of COVID-19 Screening and Diagnosis.Diagnostics (Basel, Switzerland) · 2022Review
- Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.Journal of imaging · 2022Review
- A Comprehensive Review of Machine Learning Used to Combat COVID-19.Diagnostics (Basel, Switzerland) · 2022Review
- COVID-19 image classification using deep learning: Advances, challenges and opportunities.Computers in biology and medicine · 2022Review
- The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease.Interdisciplinary sciences, computational life sciences · 2022Article
- Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights.Sensors (Basel, Switzerland) · 2022Review
- Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, X-ray, and cholesterol dataset.Scientific reports · 2022Article
- Image enhancement techniques on deep learning approaches for automated diagnosis of COVID-19 features using CXR images.Multimedia tools and applications · 2022Article
- A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19).Computers in biology and medicine · 2021Article
- Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning.BMC medical informatics and decision making · 2021Article
- Optimization in the Context of COVID-19 Prediction and Control: A Literature Review.IEEE access : practical innovations, open solutions · 2021Article
- The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic.Information systems frontiers : a journal of research and innovation · 2021Article
- A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients.Information systems frontiers : a journal of research and innovation · 2021Article
- FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia.Information systems frontiers : a journal of research and innovation · 2021Article
- COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking.Information systems frontiers : a journal of research and innovation · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the hospital, because of the rise in cases daily, there are a small number of COVID-19 test kits available. For this purpose, a rapid alternative diagnostic choice to prevent COVID-19 spread among individuals must be implemented as an automatic detection method. In this article, the multi-objective optimization and deep learning-based technique for identifying infected patients with coronavirus using X-rays is proposed. J48 decision tree approach classifies the deep feature of corona affected X-ray images for the efficient detection of infected patients. In this study, 11 different convolutional neural network-based (CNN) models (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet) are developed for detection of infected patients with coronavirus pneumonia using X-ray images. The efficiency of the proposed model is tested using k-fold cross-validation method. Moreover, the parameters of CNN deep learning model are tuned using multi-objective spotted hyena optimizer (MOSHO). Extensive analysis shows that the proposed model can classify the X-ray images at a good accuracy, precision, recall, specificity and F1-score rates. Extensive experimental results reveal that the proposed model outperforms competitive models in terms of well-known performance metrics. Hence, the proposed model is useful for real-time COVID-19 disease classification from X-ray chest images.Communicated by Ramaswamy H. Sarma.
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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.