ArticleComputers in biology and medicine2022
A lightweight CNN-based network on COVID-19 detection using X-ray and CT images.
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 17 papers.
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17 citing papers in PubMed, 90 citations in OpenAlex.
- A two-stage preprocessing and classification approach for accurate COVID-19 detection in X-ray images.Scientific reports · 2026Article
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
- CBCT radiomic features-based machine learning prediction models and nomogram for treatment decision-making regarding surgery-first approach in skeletal Class III malocclusion.BMC oral health · 2025Article
- 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
- PET/CT deep learning prognosis for treatment decision support in esophageal squamous cell carcinoma.Insights into imaging · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning.Biomedical signal processing and control · 2023Article
- Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images.Scientific reports · 2023Article
- Smart brain tumor diagnosis system utilizing deep convolutional neural networks.Multimedia tools and applications · 2023Article
- Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation.Journal of imaging · 2023Article
- A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images.Multimedia tools and applications · 2023Article
- Article
- Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images.Computers in biology and medicine · 2022Article
- An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics.Healthcare analytics (New York, N.Y.) · 2022Article
- LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images.Computers in biology and medicine · 2022Article
- A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning.Computers in biology and medicine · 2022Article
- COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention.Computers in biology and medicine · 2022Article
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2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivesThe traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, which is easy to cause identification mistakes. In this study, the technology of convolutional neural network is expected to be able to efficiently and accurately identify the COVID-19 disease.
methodsThis study uses and fine-tunes seven convolutional neural networks including InceptionV3, ResNet50V2, Xception, DenseNet121, MobileNetV2, EfficientNet-B0, and EfficientNetV2 on COVID-19 detection. In addition, we proposes a lightweight convolutional neural network, LightEfficientNetV2, on small number of chest X-ray and CT images. Five-fold cross-validation was used to evaluate the performance of each model. To confirm the performance of the proposed model, LightEfficientNetV2 was carried out on three different datasets (NIH Chest X-rays, SARS-CoV-2 and COVID-CT).
resultsOn chest X-ray image dataset, the highest accuracy 96.50% was from InceptionV3 before fine-tuning; and the highest accuracy 97.73% was from EfficientNetV2 after fine-tuning. The accuracy of the LightEfficientNetV2 model proposed in this study is 98.33% on chest X-ray image. On CT images, the best transfer learning model before fine-tuning is MobileNetV2, with an accuracy of 94.46%; the best transfer learning model after fine-tuning is Xception, with an accuracy of 96.78%. The accuracy of the LightEfficientNetV2 model proposed in this study is 97.48% on CT image.
conclusionsCompared with the SOTA, LightEfficientNetV2 proposed in this study demonstrates promising performance on chest X-ray images, CT images and three different datasets.
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