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Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm by Akmalbek Bobomirzaevich Abdusalomov; Rashid Nasimov; Nigorakhon Nasimova; Bahodir Muminov; Taeg Keun Whangbo is a Engineering article available to read on EtoBox.

What is Evaluating Synthetic Medical Images Using Artificial Intelligence with the GAN Algorithm about?

In recent years, considerable work has been conducted on the development of synthetic medical images, but there are no satisfactory methods for evaluating their medical suitability. Existing methods mainly evaluate the quality of noise in the images, and the similarity of the images to the real images used to generate them. For this purpose, they use feature maps of images extracted in different ways or distribution of images set. Then, the proximity of synthetic images to the real set is evaluated using different distance metrics. However, it is not possible to determine whether only one synthetic image was generated repeatedly, or whether the synthetic set exactly repeats the training set. In addition, most evolution metrics take a lot of time to calculate. Taking these issues into account, we have proposed a method that can quantitatively and qualitatively evaluate synthetic images. This method is a combination of two methods, namely, FMD and CNN-based evaluation methods. The estimation methods were compared with the FID method, and it was found that the FMD method has a great advantage in terms of speed, while the CNN method has the ability to estimate more accurately. To evalu

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Author

Akmalbek Bobomirzaevich Abdusalomov; Rashid Nasimov; Nigorakhon Nasimova; Bahodir Muminov; Taeg Keun Whangbo

Publisher

MDPI AG

Published

2023

Language

EN

Field

Engineering (Physical Sciences)