ArticlePeerJ. Computer science2021
An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest x-ray images.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Forecasting COVID-19 inpatient mortality using fundamental parameters in resource-constrained settings: a countrywide multi-center cohort study.BMC infectious diseases · 2025Article
- Clinical-Oriented Hierarchical Machine Learning Framework for Early Kidney Tumor Detection and Malignant Subtype Classification.Tomography (Ann Arbor, Mich.) · 2025Article
- Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images.BMC medical imaging · 2024Article
- Precise Prostate Cancer Assessment Using IVIM-Based Parametric Estimation of Blood Diffusion from DW-MRI.Bioengineering (Basel, Switzerland) · 2024Article
- A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach.PeerJ. Computer science · 2024Article
- Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- A generic optimization and learning framework for Parkinson disease via speech and handwritten records.Journal of ambient intelligence and humanized computing · 2022Article
- A complete framework for accurate recognition and prognosis of COVID-19 patients based on deep transfer learning and feature classification approach.Artificial intelligence review · 2022Article
- CovH2SD: A COVID-19 detection approach based on Harris Hawks Optimization and stacked deep learning.Expert systems with applications · 2021Article
- Hybrid COVID-19 segmentation and recognition framework (HMB-HCF) using deep learning and genetic algorithms.Artificial intelligence in medicine · 2021Article
- COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans.PeerJ. Computer science · 2021Article
- COVID-19 recognition from chest X-ray images by combining deep learning with transfer learning.Digital healthArticle
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4 authors.
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Abstract
Accurate and fast detection of COVID-19 patients is crucial to control this pandemic. Due to the scarcity of COVID-19 testing kits, especially in developing countries, there is a crucial need to rely on alternative diagnosis methods. Deep learning architectures built on image modalities can speed up the COVID-19 pneumonia classification from other types of pneumonia. The transfer learning approach is better suited to automatically detect COVID-19 cases due to the limited availability of medical images. This paper introduces an Optimized Transfer Learning-based Approach for Automatic Detection of COVID-19 (OTLD-COVID-19) that applies an optimization algorithm to twelve CNN architectures to diagnose COVID-19 cases using chest x-ray images. The OTLD-COVID-19 approach adapts Manta-Ray Foraging Optimization (MRFO) algorithm to optimize the network hyperparameters' values of the CNN architectures to improve their classification performance. The proposed dataset is collected from eight different public datasets to classify 4-class cases (COVID-19, pneumonia bacterial, pneumonia viral, and normal). The experimental result showed that DenseNet121 optimized architecture achieves the best performance. The evaluation results based on Loss, Accuracy, F1-score, Precision, Recall, Specificity, AUC, Sensitivity, IoU, and Dice values reached 0.0523, 98.47%, 0.9849, 98.50%, 98.47%, 99.50%, 0.9983, 0.9847, 0.9860, and 0.9879 respectively.
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