ArticlePloS one2021
CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 54 citations in OpenAlex.
- Multi-Modal Machine Learning Approach for COVID-19 Detection Using Biomarkers and X-Ray Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Contribution to pulmonary diseases diagnostic from X-ray images using innovative deep learning models.Heliyon · 2024Article
- Efficient pneumonia detection using Vision Transformers on chest X-rays.Scientific reports · 2024Article
- The CNN model aided the study of the clinical value hidden in the implant images.Journal of applied clinical medical physics · 2023Article
- Grad-CAM-Based Explainable Artificial Intelligence Related to Medical Text Processing.Bioengineering (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
- Explainable COVID-19 Detection Based on Chest X-rays Using an End-to-End RegNet Architecture.Viruses · 2023Article
- LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases.Sensors (Basel, Switzerland) · 2023Article
- Prediction of the COVID disease using lung CT images by Deep Learning algorithm: DETS-optimized Resnet 101 classifier.Frontiers in medicine · 2023Article
- A Survey on Deep Learning in COVID-19 Diagnosis.Journal of imaging · 2022Review
- E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network.Engineering applications of artificial intelligence · 2022Article
- Lateral epicondylitis: New trends and challenges in treatment.World journal of orthopedics · 2022Review
- AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.Computational intelligence and neuroscience · 2022Article
- Optimal scale combination selection for inconsistent multi-scale decision tables.Soft computing · 2022Article
- FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.
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