Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom.
Department of Artificial Intelligence, Rare Sense Inc, Covina, California, USA.
Stud Health Technol Inform. 2024 Aug 22;316:1145-1150. doi: 10.3233/SHTI240612.
Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study proposed a modified VGG16-based algorithm to recognise AI-generated medical images. Initially, 10,000 synthetic medical skin lesion images were generated using a Generative Adversarial Network (GAN), providing a set of images for comparison to real images. Then, an enhanced VGG16-based algorithm has been developed to classify real images vs AI-generated images. Following hyperparameters tuning and training, the optimal approach can classify the images with 99.82% accuracy. Multiple other evaluations have been used to evaluate the efficacy of the proposed network. The complete dataset used in this study is available online to the research community for future research.
通用计算机的进步使得生成高质量的合成医学图像成为可能，这些图像的质量之高，以至于人类的眼睛无法区分真实图像和 AI 生成的图像。为了分析生成的医学图像的效果，本研究提出了一种基于修改后的 VGG16 的算法来识别 AI 生成的医学图像。首先，使用生成式对抗网络（GAN）生成了 10000 张合成医学皮肤病变图像，为与真实图像进行比较提供了一组图像。然后，开发了一种增强的基于 VGG16 的算法来对真实图像和 AI 生成的图像进行分类。在进行超参数调整和训练后，最佳方法可以以 99.82%的准确率对图像进行分类。还使用了多种其他评估方法来评估所提出的网络的效果。本研究中使用的完整数据集可供研究社区在线获取，以用于未来的研究。
