ArticleComputers in biology and medicine2022
Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Artificial Intelligence Applications in Pneumonia: Diagnosis and Outcome Prediction.Current pulmonology reports · 2026Review
- Automated Detection of COVID-19 from Multimodal Imaging Data Using Optimized Convolutional Neural Network Model.Journal of imaging informatics in medicine · 2024Article
- A GPT-based EHR modeling system for unsupervised novel disease detection.Journal of biomedical informatics · 2024Article
- Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts.Scientific reports · 2024Article
- Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems.Sensors (Basel, Switzerland) · 2024Article
- Article
- A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images.Journal of digital imaging · 2023Article
- Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants.Diagnostics (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
- Improve the performance of CT-based pneumonia classification via source data reweighting.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
- Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Deep convolution neural network for screening carotid calcification in dental panoramic radiographs.PLOS digital health · 2023Article
- A fuzzy fine-tuned model for COVID-19 diagnosis.Computers in biology and medicine · 2023Article
- Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans.Intelligent systems with applications · 2023Article
- Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images.PloS one · 2023Article
- Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning.Journal of medical and biological engineering · 2023Article
- Computed tomography-based COVID-19 triage through a deep neural network using mask-weighted global average pooling.Frontiers in cellular and infection microbiology · 2023Article
- A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications.Advances in engineering software (Barking, London, England : 1992) · 2023Article
- A complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19: a retrospective study.Scientific reports · 2022Article
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2 authors.
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Abstract
Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turned out to be a pandemic affecting millions of individuals across the globe. Conducting rapid tests for a large number of suspects preventing the spread of the virus has become a challenge. In the recent past, several deep learning based approaches have been developed for automating the process of detecting COVID-19 infection from Lung Computerized Tomography (CT) scan images. However, most of them rely on a single model prediction for the final decision which may or may not be accurate. In this paper, we propose a novel ensemble approach that aggregates the strength of multiple deep neural network architectures before arriving at the final decision. We use various pre-trained models such as VGG16, VGG19, InceptionV3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, and MobileNet and fine-tune them using Lung CT Scan images. All these trained models are further used to create a strong ensemble classifier that makes the final prediction. Our experiments exhibit that the proposed ensemble approach is superior to existing ensemble approaches and set state-of-the-art results for detecting COVID-19 infection from lung CT scan images.
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