At present, the computer aided diagnosis technology based on medical imaging is at a stage of rapid development, but limited by the medical imaging data size, the modeling method based on deep learning cannot explore more complex models. Starting from the data augmentation method for medical CT images, this article summarizes the imaging characteristics of medical lesion images, classifies the existing methods for lesion detection and segmentation tasks, and expounds on the current difficulties in medical image detection and segmentation. It summarizes the related technologies of medical lesion detection, the data augmentation methods, and the lesion detection methods based on the Generative Adversarial Network (GAN). Finally, the data augmentation methods based on deep learning in the medical field, including GAN, pix2pixGAN, and CycleGAN models, are comparatively analyzed, and the future development trend of medical image analysis is prospected.