Comparison of image enhancement techniques and CNN models for COVID-19 classification using chest x-rays images

Simple item page
dc.contributor.authorKanjanasurat, Isoon
dc.contributor.authorDomepananakorn, Nontacha
dc.contributor.authorArchevapanich, Tuanjai
dc.contributor.authorPurahong, Boonchana
dc.date.accessioned2026-08-06T10:35:12Z
dc.date.available2026-08-06T10:35:12Z
dc.date.issued2022-01-01
dc.description.abstractThis paper compares two image enhancement techniques with five convolutional neural network (CNN) models to classify Covid-19 chest x-ray images. a contrast limited adaptive histogram (CLAHE) and gamma correction which is method to improve image histogram are compared with the original chest x-ray image. We use five publicly available pre-trained CNN models to detect COVID-19: MobileNet, MobileNetV2, DenseNet169, DenseNet201, and ResNet50V2. Our procedure was validated using the COVID-19 radiography database, which is a freely accessible resource. MoblileNet with gamma correction is well-suited for COVIC-19 classification, achieving an accuracy score of 87.53 percent on the first epoch and 95.46 percent after training 100 epochs with the shortest computation time.
dc.identifier.citation8th International Conference on Engineering Applied Sciences and Technology Iceast 2022 Proceedings, 6-9, 2022
dc.identifier.doi10.1109/ICEAST55249.2022.9826319
dc.identifier.other2-s2.0-85135929230
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12739
dc.source8th International Conference on Engineering Applied Sciences and Technology Iceast 2022 Proceedings
dc.subjectCLAHE
dc.subjectConvolutional Neural Network
dc.subjectCOVID-19
dc.subjectImage enhancement
dc.titleComparison of image enhancement techniques and CNN models for COVID-19 classification using chest x-rays images
dc.typeConference Paper

Files

Collections

Research