ArticleComputational intelligence and neuroscience2022
ADA-COVID: Adversarial Deep Domain Adaptation-Based Diagnosis of COVID-19 from Lung CT Scans Using Triplet Embeddings.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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9 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- Application of machine learning in breast cancer survival prediction using a multimethod approach.Scientific reports · 2024Article
- Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images.Scientific reports · 2024Article
- CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images.BMC medical informatics and decision making · 2023Article
- AMTLDC: a new adversarial multi-source transfer learning framework to diagnosis of COVID-19.Evolving systems · 2023Article
- Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.Frontiers in oncology · 2023Article
- Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models.Computational and mathematical methods in medicine · 2022Article
- Diagnosis of COVID-19 Disease in Chest CT-Scan Images Based on Combination of Low-Level Texture Analysis and MobileNetV2 Features.Computational intelligence and neuroscience · 2022Article
- AI-CenterNet CXR: An artificial intelligence (AI) enabled system for localization and classification of chest X-ray disease.Frontiers in medicine · 2022Article
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3 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Rapid diagnosis of COVID-19 with high reliability is essential in the early stages. To this end, recent research often uses medical imaging combined with machine vision methods to diagnose COVID-19. However, the scarcity of medical images and the inherent differences in existing datasets that arise from different medical imaging tools, methods, and specialists may affect the generalization of machine learning-based methods. Also, most of these methods are trained and tested on the same dataset, reducing the generalizability and causing low reliability of the obtained model in real-world applications. This paper introduces an adversarial deep domain adaptation-based approach for diagnosing COVID-19 from lung CT scan images, termed ADA-COVID. Domain adaptation-based training process receives multiple datasets with different input domains to generate domain-invariant representations for medical images. Also, due to the excessive structural similarity of medical images compared to other image data in machine vision tasks, we use the triplet loss function to generate similar representations for samples of the same class (infected cases). The performance of ADA-COVID is evaluated and compared with other state-of-the-art COVID-19 diagnosis algorithms. The obtained results indicate that ADA-COVID achieves classification improvements of at least 3%, 20%, 20%, and 11% in accuracy, precision, recall, and F
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