ArticleComplex & intelligent systems2021
Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans.
Article in Complex & intelligent systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 141 citations in OpenAlex.
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- Disease detection and classification in temporal lobe epilepsy: step-wise versus simultaneous AI decision models in a multisite neuroimaging study.Brain communications · 2026Article
- Topological data analysis and machine learning for COVID-19 detection in CT scan lung images.BMC biomedical engineering · 2025Article
- Evaluation of Deep Learning Methods for Pulmonary Disease Classification.Current medical imaging · 2025Article
- Concatenated CNN-Based Pneumonia Detection Using a Fuzzy-Enhanced Dataset.Sensors (Basel, Switzerland) · 2024Article
- Advances of surgical robotics: image-guided classification and application.National science review · 2024Review
- Texture-Based Classification to Overcome Uncertainty between COVID-19 and Viral Pneumonia Using Machine Learning and Deep Learning Techniques.Diagnostics (Basel, Switzerland) · 2024Article
- Concatenated Modified LeNet Approach for Classifying Pneumonia Images.Journal of personalized medicine · 2024Article
- Hybrid Diagnostic Model for Improved COVID-19 Detection in Lung Radiographs Using Deep and Traditional Features.Biomimetics (Basel, Switzerland) · 2023Article
- Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.Biomedical signal processing and control · 2023Article
- Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography.Healthcare (Basel, Switzerland) · 2023Article
- A comprehensive review on variants of SARS-CoVs-2: Challenges, solutions and open issues.Computer communications · 2023Article
- Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images.Biomedical signal processing and control · 2023Article
- Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19.Materials today. Proceedings · 2023Article
- RETRACTED ARTICLE: Establishment of Neural Series Transmission Keys (NSTKs) and Security in the Field of COVID-19 Telepediatric Oral Health.Wireless personal communications · 2023Article
- ABOA-CNN: auction-based optimization algorithm with convolutional neural network for pulmonary disease prediction.Neural computing & applications · 2023Article
- A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images.Multimedia tools and applications · 2023Article
- AI for COVID-19 Detection from Radiographs: Incisive Analysis of State of the Art Techniques, Key Challenges and Future Directions.Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research · 2022Review
- Comprehensive Survey of Machine Learning Systems for COVID-19 Detection.Journal of imaging · 2022Review
- COVID-19 chest X-ray detection through blending ensemble of CNN snapshots.Biomedical signal processing and control · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided diagnosis (CAD) systems are considered a powerful tool for physicians to support identification of the novel Coronavirus Disease 2019 (COVID-19) using medical imaging modalities. Therefore, this article proposes a new framework of cascaded deep learning classifiers to enhance the performance of these CAD systems for highly suspected COVID-19 and pneumonia diseases in X-ray images. Our proposed deep learning framework constitutes two major advancements as follows. First, complicated multi-label classification of X-ray images have been simplified using a series of binary classifiers for each tested case of the health status. That mimics the clinical situation to diagnose potential diseases for a patient. Second, the cascaded architecture of COVID-19 and pneumonia classifiers is flexible to use different fine-tuned deep learning models simultaneously, achieving the best performance of confirming infected cases. This study includes eleven pre-trained convolutional neural network models, such as Visual Geometry Group Network (VGG) and Residual Neural Network (ResNet). They have been successfully tested and evaluated on public X-ray image dataset for normal and three diseased cases. The results of proposed cascaded classifiers showed that VGG16, ResNet50V2, and Dense Neural Network (DenseNet169) models achieved the best detection accuracy of COVID-19, viral (Non-COVID-19) pneumonia, and bacterial pneumonia images, respectively. Furthermore, the performance of our cascaded deep learning classifiers is superior to other multi-label classification methods of COVID-19 and pneumonia diseases in previous studies. Therefore, the proposed deep learning framework presents a good option to be applied in the clinical routine to assist the diagnostic procedures of COVID-19 infection.
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