Scholarlink

This paper investigates the usage of generative opposed networks (GANs) and recurrent neural networks (RNNs) for medical photo segmentation. First, clinical picture segmentation is described and discussed, after which the two fundamental methods of segmentation, particularly supervised and unsupervised studying, are reviewed. Most generative models are reviewed, including generative opposed networks (GANs) and recursive neural networks (RNNs). Primarily, the paper specializes in utilizing GANs and RNNs for scientific image segmentation. The contribution of this paper is threefold. First, it provides an in-depth review of the nation of the artwork in generative fashions, focusing on the software of GANs and RNNs for scientific photograph segmentation. 2nd, it provides a discussion of the challenges and possibilities of the usage of GANs and RNNs for medical image segmentation.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Investigating the Use of Generative Adversarial Networks and Recurrent Neural Networks for Medical Image Segmentation

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Deepak Kumar,
  • Bhawna Wadhwa,
  • Ramkumar Krishnamoorthy,
  • Ankita Agarwal

摘要

This paper investigates the usage of generative opposed networks (GANs) and recurrent neural networks (RNNs) for medical photo segmentation. First, clinical picture segmentation is described and discussed, after which the two fundamental methods of segmentation, particularly supervised and unsupervised studying, are reviewed. Most generative models are reviewed, including generative opposed networks (GANs) and recursive neural networks (RNNs). Primarily, the paper specializes in utilizing GANs and RNNs for scientific image segmentation. The contribution of this paper is threefold. First, it provides an in-depth review of the nation of the artwork in generative fashions, focusing on the software of GANs and RNNs for scientific photograph segmentation. 2nd, it provides a discussion of the challenges and possibilities of the usage of GANs and RNNs for medical image segmentation.