Reference Hub

This research has been cited in:

Chapter
Accuration of Classification of Covid with Convolutional Neural Network-Based Image Chest X-ray with Variations in Image Size and Batch SizeProceeding of the 3rd International Conference on Electronics, Biomedical Engineering, and Health Informatics10.1007/978-981-99-0248-4_13
Conference
A Mental Health Performance Assessment using Support Vector Machine2023 3rd International Conference on Intelligent Technologies (CONIT)10.1109/CONIT59222.2023.10205772
Article
Application of Convolutional Neural Network to Gripping Comfort Evaluation Using Gripping Posture ImageJournal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2023.p0655
Article
PulmoNet: a novel deep learning based pulmonary diseases detection modelBMC Medical Imaging10.1186/s12880-024-01227-2
Article
An AI-based Image Recognition System for Early Detection of Forest and Field FiresEuropean Journal of Forest Engineering10.33904/ejfe.1322396
Chapter
Electroencephalogram Analysis Using Convolutional Neural Networks in Order to Diagnose Alzheimer’s DiseaseData Analysis for Neurodegenerative Disorders10.1007/978-981-99-2154-6_7
Conference
Application of a new multi-binary classification strategy in the Weld Defects Images2023 IEEE International Workshop on Mechatronic Systems Supervision (IW_MSS)10.1109/IW_MSS59200.2023.10368597
Conference
Detection of Covid Disease using Computed Tomography Images2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT56998.2023.10306637
Conference
Enhancing Credit Card Fraud Detection: Analyzing Time and Amount Distributions with Computational Intelligence Algorithms2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)10.1109/ICUIS60567.2023.00061
Article
A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing DataRemote Sensing10.3390/rs14040819
Article
A fuzzy fine-tuned model for COVID-19 diagnosisComputers in Biology and Medicine10.1016/j.compbiomed.2022.106483
Article
Transfer and Deep Learning-Based Gurmukhi Handwritten Word Classification ModelMathematical Problems in Engineering10.1155/2023/4768630
Article
RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal imagesScientific Reports10.1038/s41598-023-42111-3
Article
Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR ScansComputational Intelligence and Neuroscience10.1155/2022/7124199
Article
Potential diagnostic application of a novel deep learning- based approach for COVID-19Scientific Reports10.1038/s41598-023-50742-9
Article
CIA-CVD: cloud based image analysis for COVID-19 vaccination distributionJournal of Cloud Computing10.1186/s13677-023-00539-y
Article
COVID-19 infection segmentation using hybrid deep learning and image processing techniquesScientific Reports10.1038/s41598-023-49337-1
Article
Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning SystemJournal of Personalized Medicine10.3390/jpm12040535
Article
Prediction of Diabetes through Retinal Images Using Deep Neural NetworkComputational Intelligence and Neuroscience10.1155/2022/7887908
Article
Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule NetworkComputational Intelligence and Neuroscience10.1155/2022/6185013
Article
Transfer Learning with Feature Extraction Modules for Improved Classifier Performance on Medical Image DataScientific Programming10.1155/2022/4983174

Binary Classification of COVID-19 CT Images Using CNN: COVID Diagnosis Using CT

Shankar Shambhu, Deepika Koundal, Prasenjit Das, Chetan Sharma

Source Title: International Journal of E-Health and Medical Communications (IJEHMC)13(2)

ISSN: 1947-315X|EISSN: 1947-3168|EISBN13: 9781799896821|DOI: 10.4018/IJEHMC.20220701.oa4

Cite Article Cite Article

MLA

Shambhu, Shankar, et al. "Binary Classification of COVID-19 CT Images Using CNN: COVID Diagnosis Using CT." IJEHMC vol.13, no.2 2022: pp.1-13. http://doi.org/10.4018/IJEHMC.20220701.oa4

APA

Shambhu, S., Koundal, D., Das, P., & Sharma, C. (2022). Binary Classification of COVID-19 CT Images Using CNN: COVID Diagnosis Using CT. International Journal of E-Health and Medical Communications (IJEHMC), 13(2), 1-13. http://doi.org/10.4018/IJEHMC.20220701.oa4

Chicago

Shambhu, Shankar, et al. "Binary Classification of COVID-19 CT Images Using CNN: COVID Diagnosis Using CT," International Journal of E-Health and Medical Communications (IJEHMC) 13, no.2: 1-13. http://doi.org/10.4018/IJEHMC.20220701.oa4

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

COVID-19 pandemic has hit the world with such a force that the world's leading economies are finding it challenging to come out of it. Countries with the best medical facilities are even cannot handle the increasing number of cases and fatalities. This disease causes significant damage to the lungs and respiratory system of humans, leading to their death. Computed tomography (CT) images of the respiratory system are analyzed in the proposed work to classify the infected people with non-infected people. Deep learning binary classification algorithms have been applied, which have shown an accuracy of 86.9% on 746 CT images of chest having COVID-19 related symptoms.