通过使用人工神经网络的模糊图形覆盖数来预测癌症的性质
Anushree Bhattacharya1, Madhumangal Pal1
1Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapur, W.B. 721102, India.
Artificial intelligence in medicine
|February 7, 2024
概括
这项研究引入了模糊的图形状盖,用于预测癌症,利用人工神经网络. 乳腺癌被确定为该案例研究中最可能发生的类型,为医疗决策提供了一个新的工具.
科学领域:
- 图形理论 图形理论
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 癌症预测在医疗保健中至关重要.
- 信号通路影响癌症风险.
- 现有的预测方法与不确定性作斗争.
研究的目的:
- 为了在癌症预测中模拟不确定性,引入模糊的图形状盖.
- 为了开发一个新的参数,模糊的图形形覆盖数.
- 为了创建一个高效的算法,寻找模糊的图形覆盖集.
主要方法:
- 模糊图形理论用于模拟生物途径的应用.
- 模糊的图形状覆盖号的发展和特征.
- 实现一个高效的算法与复杂性分析.
- 与人工神经网络 (1D和2D) 集成,用于癌症预测.
主要成果:
- 对于模糊的图形状覆盖号的确定的表征和极限值.
- 开发了一个有效的算法,用于模糊的图形状覆盖集识别.
- 通过人工神经网络成功模拟了癌症预测.
- 在案例研究中确定乳腺癌是最有可能发生的类型.
结论:
- 模糊的图形状盖子为不确定的癌症预测提供了一个强大的框架.
- 提出的方法和算法为医疗决策提供了稳定高效的工具.
- 这项研究强调了图形理论在推进癌症诊断方面的潜力.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.2K
03:05Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
1.1K
相关概念视频
Cancer Survival Analysis
348
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
348
Protein Networks
4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K
