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Comparison of image enhancement techniques and CNN models for COVID-19 classification using chest x-rays images
Author(s)
Date Issued
January 1, 2022
Type
Conference Paper
Abstract
This 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.
Citation
8th International Conference on Engineering Applied Sciences and Technology Iceast 2022 Proceedings, 6-9, 2022