The booming development in the field of artificial intelligence has brought about many innovations and breakthroughs, and one of the technologies that has attracted much attention is Imagex. Imagex, as an image processing technology based on artificial intelligence, has been applied and verified in many fields. In order to better understand the development and application of Imagex, this article will introduce some important Imagex references and reviews to provide readers with a comprehensive knowledge reference. 1. References: - He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition ( pp. 770-778). This paper introduces the ResNet model, which is an important breakthrough in the field of deep learning. ResNet solves the problem of difficulty in training deep networks through residual learning, which is of great significance for image processing tasks such as Imagex. - Krizhevsky, A., Sutskever, I., & Hinton, GE (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). This paper introduces the AlexNet model, It is an important breakthrough in deep learning in image classification tasks. The success of AlexNet established the status of deep convolutional neural networks in image processing and provided a foundation for research on tasks such as Imagex. - Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. This paper introduces the VGG model, another important volume in deep learning Accumulated neural network model. The structure of VGG is simple and clear. It improves performance by increasing network depth and achieves excellent results in image processing tasks such as Imagex. 2. Review: - Litjens, G., Kooi, T., Bejnordi, BE, Setio, AA, Ciompi, F., Ghafoorian, M., ... & Sanchez, CI (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88. This review provides a comprehensive overview of the application of deep learning in the field of medical image analysis. By summarizing and analyzing the application of various deep learning models in medical image processing, it provides reference and guidance for Imagex's research in the field of medical imaging. - Zhang, X., Zhou, X., Lin, M., & Sun, J. (2019). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recoition ( pp. 6848-6856). This review introduces the ShuffleNet model, an efficient convolutional neural network for mobile devices. This review discusses the structure and performance of ShuffleNet in detail, providing reference and guidance for the application of Imagex on mobile devices. Conclusion: Imagex, as an image processing technology, has broad application potential in the field of artificial intelligence. By in-depth studying the relevant references and reviews of Imagex, we can better understand and master the development and application of this technology. We hope that the references and reviews introduced in this article can provide readers with a comprehensive knowledge reference and help them understand and apply Imagex technology more deeply.