Expansion dataset COVID-19 chest X-ray using data augmentation and histogram equalization

Abstract

The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

Future Iris Imaging with Advanced Fuzzified Histogram Equalization

“…An advanced recognition system in [19] proposed a Convolutional Neural Network (CNN) with HE and CLAHE to efficiently enhance and detect COVID-19 diseases in chest Xray images. AlKhalid in [20] proposed the same model; CNN combined with HE and CLAHE using COVID-19 chest Xray images for data expansion, transformation, and enhancement. Two layers of HE are applied to seven layers of data transformation; however, the study begins with a conceptual hashing algorithm to eliminate duplicate images.…”

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