Comparison of image enhancement techniques and CNN models for COVID-19 classification using chest x-rays images - Isoon Kanjanasurat; Nontacha Domepananakorn; Tuanjai Archevapanich; Boonchana Purahong (My University, 2022)
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.
Keywords: COVID-19 diagnosis using AI; COVID-19 Clinical Research Studies; Anomaly Detection Techniques and Applications