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For generating high-quality, large-scale image there is an effective technique for such a work called Generative Adversarial Networks (GAN). To develop the automatic diagnostic tool which will assist to medical practitioner there is a basic need to collect the medical data. The existing various data collection techniques are very costly and time consuming due to the utilization of high-radiation. One of the most popular techniques is GAN which comes under the category of Deep learning technique. It translates low-resolution images into high-resolutions such as MRIs can be generated from CT scans, and 7T images can be created from 3T MRIs. This technique can be used to create multimodal datasets from a single modality. In this piece of work various GAN architecture covered for the purpose of medical image analysis.

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A Comparative Analysis of Medical Images Generation Using Various GAN Techniques

  • 会议论文

  • 发表日期：

    2026年

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  • Naveen Kumar Gupta,
  • Prashant Shukla

摘要

For generating high-quality, large-scale image there is an effective technique for such a work called Generative Adversarial Networks (GAN). To develop the automatic diagnostic tool which will assist to medical practitioner there is a basic need to collect the medical data. The existing various data collection techniques are very costly and time consuming due to the utilization of high-radiation. One of the most popular techniques is GAN which comes under the category of Deep learning technique. It translates low-resolution images into high-resolutions such as MRIs can be generated from CT scans, and 7T images can be created from 3T MRIs. This technique can be used to create multimodal datasets from a single modality. In this piece of work various GAN architecture covered for the purpose of medical image analysis.