COVID-19 digital x-rays forgery classification model using deep learning - Eman I. Abd El-Latif; Nour Eldeen Khalifa (IAES International Journal of Artificial Intelligence (IJ-AI), 2023)
Nowadays, the internet has become a typical medium for sharing digitalimages through web applications or social media and there was a rise inconcerns about digital image privacy. Image editing softwareâ€™s have preparedit incredibly simple to make changes to an image's content without leavingany visible evidence for images in general and medical images in particular.In this paper, the COVID-19 digital x-rays forgery classification modelutilizing deep learning will be introduced. The proposed system will be ableto identify and classify image forgery (copy-move and splicing) manipulation.Alexnet, Resnet50, and Googlenet are used in this model for feature extractionand classification, respectively. Images have been tampered with in threeclasses (COVID-19, viral pneumonia, and normal). For the classification of(Forgery or no forgery), the model achieves 0.9472 in testing accuracy. Forthe classification of (Copy-move forgery, splicing forgery, and no forgery),the model achieves 0.8066 in testing accuracy. Moreover, the model achieves0.796 and 0.8382 for 6 classes and 9 classes problems respectively.Performance indicators like Recall, Precision, and F1 Score supported theachieved results and proved that the proposed system is efficient for detectingthe manipulation in images.Â