ArticleBiocybernetics and biomedical engineering
Automated detection of COVID-19 from CT scan using convolutional neural network.
Article in Biocybernetics and biomedical engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Explainable Deep Learning Framework for Binary Corrosion Image Classification Using Grad-CAM.Sensors (Basel, Switzerland) · 2025Article
- The possibilities of prospective assessment of SARS-CoV-2 infection risk based on an artificial neural network model - a cross-sectional andArchives of medical science : AMS · 2025Article
- Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.Scientific reports · 2023Article
- Deep Learning-Based Classification of Chest Diseases Using X-rays, CT Scans, and Cough Sound Images.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.Biocybernetics and biomedical engineering · 2023Article
- Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images.Intelligent systems with applications · 2023Article
- Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review.Sensors (Basel, Switzerland) · 2022Review
- Classification of Pulmonary Damage Stages Caused by COVID-19 Disease from CT Scans via Transfer Learning.Bioengineering (Basel, Switzerland) · 2022Article
- LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images.International journal of imaging systems and technology · 2022Article
- Automatic classification of brain magnetic resonance images with hypercolumn deep features and machine learning.Physical and engineering sciences in medicine · 2022Article
- Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network.Physical and engineering sciences in medicine · 2022Article
- COVID-19 image classification using deep learning: Advances, challenges and opportunities.Computers in biology and medicine · 2022Review
- Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease.PloS one · 2022Article
- Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier.Arabian journal for science and engineering · 2022Article
- Multiclass Convolution Neural Network for Classification of COVID-19 CT Images.Computational intelligence and neuroscience · 2022Article
- COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques.Computational and mathematical methods in medicine · 2022Article
- Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview.Healthcare (Basel, Switzerland) · 2021Review
- TL-med: A Two-stage transfer learning recognition model for medical images of COVID-19.Biocybernetics and biomedical engineeringArticle
- Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network.Biocybernetics and biomedical engineeringArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model's diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.
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