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@article{155896,
        author = {Dalal Rajni Rajnish and Samarth Shukla and Vedansh Dixit and Jayesh Nahar and Trupti Suryawanshi},
        title = {Medical image synthesis using GAN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {2},
        pages = {406-410},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155896},
        abstract = {Medical images are usually utilized in clinics to produce visual representations of under-skin tissues in human bodies. Medical image synthesis strategies are developed to supply pictures that are very accurate and reasonable.  To produce diverse modalities of medical imaging with unique characteristics of visualization, different imaging protocols are used. The scanning of high-quality single modality images or homogeneous multiple modalities of images is very costly These strategies may be accustomed produce pictures of various varieties, counting on the particular desires of a given medical scenario. Among the various deep learning approaches, GANs and CycleGAN became significantly dominant for medical image synthesis in recent years. GAN and CycleGAN has provided new framework for medical image synthesis. this is often as a result of GANs offer a replacement technology and framework for the appliance of medical pictures. GANs do not need loads of labelled data to get correct data, which might be generated through competition between the generator and discriminator networks. Therefore, GANs are quickly proving to be a robust tool for machine learning and AI. X-rays, which create ionising radiation, are used in CT scans. According to research, this form of radiation may harm DNA and cause cancer. In this study, we present a method for converting an organ's MRI scan into a CT scan using cycleGan and a generative adversarial network.},
        keywords = {},
        month = {},
        }