ArticlePattern recognition2021
Deep neural networks ensemble to detect COVID-19 from CT scans.
Article in Pattern recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.Scientific reports · 2023Article
- 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
- A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images.PloS one · 2023Article
- Detection of COVID-19 Case from Chest CT Images Using Deformable Deep Convolutional Neural Network.Journal of healthcare engineering · 2023Article
- A systematic review on artificial intelligence techniques for detecting thyroid diseases.PeerJ. Computer science · 2023Article
- Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection.Computers in biology and medicine · 2022Article
- Real-Time Abnormal Object Detection for Video Surveillance in Smart Cities.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research on Coronavirus Disease 2019 (COVID-19) detection methods has increased in the last months as more accurate automated toolkits are required. Recent studies show that CT scan images contain useful information to detect the COVID-19 disease. However, the scarcity of large and well balanced datasets limits the possibility of using detection approaches in real diagnostic contexts as they are unable to generalize. Indeed, the performance of these models quickly becomes inadequate when applied to samples captured in different contexts (e.g., different equipment or populations) from those used in the training phase. In this paper, a novel ensemble-based approach for more accurate COVID-19 disease detection using CT scan images is proposed. This work exploits transfer learning using pre-trained deep networks (e.g., VGG, Xception, and ResNet) evolved with a genetic algorithm, combined into an ensemble architecture for the classification of clustered images of lung lobes. The study is validated on a new dataset obtained as an integration of existing ones. The results of the experimental evaluation show that the ensemble classifier ensures effective performance, also exhibiting better generalization capabilities.
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