ArticleBiomedical signal processing and control2023
Deep learning models-based CT-scan image classification for automated screening of COVID-19.
Article in Biomedical signal processing and control, 2023. 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, 105 citations in OpenAlex.
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- Harnessing formaldehyde detection: novel metal-doped coronene sensors to combat pollution and enable early lung cancer diagnosis.Journal of computer-aided molecular design · 2026Article
- A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.Scientific reports · 2026Article
- Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.Journal of translational medicine · 2025Article
- Homology-feature-assisted quantification of fibrotic lesions in computed tomography images: a proof of concept for CT image feature-based prediction for gene-expression-distribution.International journal of computer assisted radiology and surgery · 2025Article
- Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection.Scientific reports · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- RETRACTED: LGD_Net: Capsule network with extreme learning machine for classification of lung diseases using CT scans.PloS one · 2025Article
- Adaptive dual-window enhancement and multi-scale texture prior fusion for robust kidney CT classification.PloS one · 2025Article
- Classification of Severity of Lung Parenchyma Using Saliency and Discrete Cosine Transform Energy in Computed Tomography of Patients With COVID-19.International journal of telemedicine and applications · 2025Article
- Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images.Scientific reports · 2024Article
- Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art.European journal of nuclear medicine and molecular imaging · 2024Review
- Oil painting teaching design based on the mobile platform in higher art education.Scientific reports · 2024Article
- Deep Learning-Based Automated Measurement of Murine Bone Length in Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- Lung Disease Detection Using U-Net Feature Extractor Cascaded by Graph Convolutional Network.Diagnostics (Basel, Switzerland) · 2024Article
- Coronavirus Anatomy and Its Analytical Approaches for Targeting COVID-19.Advances in experimental medicine and biology · 2024Review
- Respiratory pathogenic microbial infections: a narrative review.International journal of medical sciences · 2024Review
- A Novel COVID-19 Diagnosis Approach Utilizing a Comprehensive Set of Diagnostic Information (CSDI).Journal of clinical medicine · 2023Article
- Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.Diagnostics (Basel, Switzerland) · 2023Article
- Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely infects the lungs and the upper respiratory tract of the human body. This virus badly affected the lives and wellness of millions of people worldwide and spread widely. Early diagnosis, timely treatment, and proper confinement of the infected patients are some possible ways to control the spreading of coronavirus. Computed tomography (CT) scanning has proven useful in diagnosing several respiratory lung problems, including COVID-19 infections. Automated detection of COVID-19 using chest CT-scan images may reduce the clinician's load and save the lives of thousands of people. This study proposes a robust framework for the automated screening of COVID-19 using chest CT-scan images and deep learning-based techniques. In this work, a publically accessible CT-scan image dataset (contains the 1252 COVID-19 and 1230 non-COVID chest CT images), two pre-trained deep learning models (DLMs) namely, MobileNetV2 and DarkNet19, and a newly-designed lightweight DLM, are utilized for the automated screening of COVID-19. A repeated ten-fold holdout validation method is utilized for the training, validation, and testing of DLMs. The highest classification accuracy of 98.91% is achieved using transfer-learned DarkNet19. The proposed framework is ready to be tested with more CT images. The simulation results with the publicly available COVID-19 CT scan image dataset are included to show the effectiveness of the presented study.
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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.