ArticleJournal of healthcare engineering2021
Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- A Multimodal Approach for Detection and Assessment of Depression Using Text, Audio and Video.Phenomics (Cham, Switzerland) · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model.Healthcare (Basel, Switzerland) · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- A Review Paper about Deep Learning for Medical Image Analysis.Computational and mathematical methods in medicine · 2023Review
- Classification of Diabetic Retinopathy Severity in Fundus Images Using the Vision Transformer and Residual Attention.Computational intelligence and neuroscience · 2023Article
- LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.Multimedia tools and applications · 2023Article
- Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images.Scientific reports · 2022Article
- CAD systems for COVID-19 diagnosis and disease stage classification by segmentation of infected regions from CT images.BMC bioinformatics · 2022Article
- Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT.Expert systems with applications · 2022Article
- Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model.Journal of healthcare engineering · 2022Article
- Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.Journal of healthcare engineering · 2022Article
- Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm.Journal of healthcare engineering · 2022Article
- Real-time internet of medical things framework for early detection of Covid-19.Neural computing & applications · 2022Article
- Intelligence Classification Algorithm-Based Drug-Resistant Pulmonary Tuberculosis Computed Tomography Imaging Features and Influencing Factors.Computational intelligence and neuroscience · 2022Article
- A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images.Journal of healthcare engineering · 2021Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks.
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Registered trials
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.