Generating Synthetic Images for Healthcare with Novel Deep Pix2Pix GAN

被引：24

作者：

Aljohani, Abeer ^{[1
]}

Alharbe, Nawaf ^{[1
]}

机构：

[1] Taibah Univ, Appl Coll, Comp Sci Dept, Madinah 46537, Saudi Arabia

来源：

ELECTRONICS | 2022年 / 11卷 / 21期

关键词：

GAN; Cyc1eGAN; Pix2Pix GAN; medical images; image synthesis; image transmission;

D O I：

10.3390/electronics11213470

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Due to recent developments in deep learning and artificial intelligence, the healthcare industry is currently going through a significant upheaval. Despite a considerable advance in medical imaging and diagnostics, the healthcare industry still has a lot of unresolved problems and unexplored applications. The transmission of a huge number of medical images in particular is a difficult and time-consuming problem. In addition, obtaining new medical images is too expensive. To tackle these issues, we propose deep pix2pix generative adversarial networks (GAN) for generating synthetic medical images. For the comparison, we implemented CycleGAN, Pix2Pix GAN and Deep Pix2Pix GAN. The result has shown that our proposed approach can generate a new synthetic medical image from a different image with more accuracy than that of the other models. To provide a robust model, we trained and evaluated our models on a widely used brain image dataset, the IXI Dataset.

引用

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