ArticleSoft computing2023
Evolving deep convolutional neutral network by hybrid sine-cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images.
Article in Soft computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Article
- An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study.Heliyon · 2024Article
- Article
- Convolutional neural network-based classification and monitoring models for lung cancer detection: 3D perspective approach.Heliyon · 2023Article
- Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images.Expert systems with applications · 2023Article
- COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review.Bioengineering (Basel, Switzerland) · 2023Review
- Fitness dependent optimizer with neural networks for COVID-19 patients.Computer methods and programs in biomedicine update · 2023Article
- Prognostic Nutritional Index, Controlling Nutritional Status (CONUT) Score, and Inflammatory Biomarkers as Predictors of Deep Vein Thrombosis, Acute Pulmonary Embolism, and Mortality in COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2022Article
- COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images.Healthcare (Basel, Switzerland) · 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
- BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images.Sensors (Basel, Switzerland) · 2022Article
- Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images.Diagnostics (Basel, Switzerland) · 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
- Efficacy and safety testing of a COVID-19 era emergency ventilator in a healthy rabbit lung model.BMC biomedical engineering · 2022Article
- Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-Ray Images Using Deep Convolutional Neural Networks Fine-Tuned by Whale Optimizer.Wireless personal communications · 2022Article
- Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System.Computational intelligence and neuroscience · 2022Article
- Intelligent computing on time-series data analysis and prediction of COVID-19 pandemics.Pattern recognition letters · 2021Article
Corrections and comments
- Retraction · 2023-05-29Concerns/Issues about Referencing/Attributions · Compromised Peer Review · Rogue Editor · Unreliable Results and/or Conclusions ·
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine-cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s.
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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.