ArticleMultimedia systems2022
Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images.
Article in Multimedia systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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40 citing papers in PubMed.
- Classification of pulmonary disorders using pulmonary network with explainable AI framework from chest multi-modal images.Scientific reports · 2026Article
- Entropy-regularized dual-stream attention fusion for multi-disease lung classification.Scientific reports · 2026Article
- Artificial Intelligence Integrated Smart Medical Imaging Lab Framework for Enhanced Diagnosis and Treatment of Pandemic-Prone Diseases.Health science reports · 2026Article
- A Flexible Multi-Channel Deep Network Leveraging Texture and Spatial Features for Diagnosing New COVID-19 Variants in Lung CT Scans.Tomography (Ann Arbor, Mich.) · 2025Article
- Joint enhancement of automatic chest x-ray diagnosis and radiological gaze prediction with multistage cooperative learning.Medical physics · 2025Article
- An automated privacy-preserving self-supervised classification of COVID-19 from lung CT scan images minimizing the requirements of large data annotation.Scientific reports · 2025Article
- Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images.Scientific reports · 2024Article
- FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.Medical & biological engineering & computing · 2024Article
- Three-Stage Framework for Accurate Pediatric Chest X-ray Diagnosis Using Self-Supervision and Transfer Learning on Small Datasets.Diagnostics (Basel, Switzerland) · 2024Article
- A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images.Heliyon · 2024Article
- CNNs trained with adult data are useful in pediatrics. A pneumonia classification example.PloS one · 2024Article
- RETRACTED: Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds.PloS one · 2024Article
- Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model.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
- Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods.Asian Pacific journal of cancer prevention : APJCP · 2023Article
- Self-paced Multi-view Learning for CT-based severity assessment of COVID-19.Biomedical signal processing and control · 2023Article
- Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images.Healthcare (Basel, Switzerland) · 2023Article
- DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.Sensors (Basel, Switzerland) · 2023Article
- Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.New generation computing · 2023Article
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4 authors.
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Abstract
Literature survey shows that convolutional neural network (CNN)-based pretrained models have been largely used for CoronaVirus Disease 2019 (COVID-19) classification using chest X-ray (CXR) and computed tomography (CT) datasets. However, most of the methods have used a smaller number of data samples for both CT and CXR datasets for training, validation, and testing. As a result, the model might have shown good performance during testing, but this type of model will not be more effective on unseen COVID-19 data samples. Generalization is an important term to be considered while designing a classifier that can perform well on completely unseen datasets. Here, this work proposes a large-scale learning with stacked ensemble meta-classifier and deep learning-based feature fusion approach for COVID-19 classification. The features from the penultimate layer (global average pooling) of EfficientNet-based pretrained models were extracted and the dimensionality of the extracted features reduced using kernel principal component analysis (PCA). Next, a feature fusion approach was employed to merge the features of various extracted features. Finally, a stacked ensemble meta-classifier-based approach was used for classification. It is a two-stage approach. In the first stage, random forest and support vector machine (SVM) were applied for prediction, then aggregated and fed into the second stage. The second stage includes logistic regression classifier that classifies the data sample of CT and CXR into either COVID-19 or Non-COVID-19. The proposed model was tested using large CT and CXR datasets, which are publicly available. The performance of the proposed model was compared with various existing CNN-based pretrained models. The proposed model outperformed the existing methods and can be used as a tool for point-of-care diagnosis by healthcare professionals.
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