大脑瘤分类用于结合多层密集的基于网络的特征提取和超参数的优势,调整了使用野马优化优化的专注的双余生成对抗网络分类器
Shenbagarajan Anantharajan1, Shenbagalakshmi Gunasekaran2, J Angela Jennifa Sujana3
1Associate Professor, Department of Artificial Intelligence and Data Science, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India.
NMR in biomedicine
|August 29, 2024
概括
一个用野马优化算法 (ADRGAN-WHOA-BTD) 优化的新型专注双残余生成对抗网络在脑瘤检测中实现了高精度. 这种方法显著优于现有技术,为医学图像分析提供了有前途的进步.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 脑瘤检测对于有效的治疗计划至关重要.
- 在神经瘤学中,准确高效的诊断工具是必不可少的.
- 现有的方法在精度和速度方面面临挑战.
研究的目的:
- 提出一个先进的深度学习模型,用于自动化脑瘤检测.
- 为了提高脑瘤分类的准确性和效率.
- 为深度学习模型参数调整引入一种新的优化算法.
主要方法:
- 使用双树复杂波波变换 (DTCWT) 进行图像预处理.
- 使用多层密集网络提取的图像特征.
- 开发了一个专注的双余生成对抗网络 (ADRGAN) 分类器.
- 用野马优化算法 (WHOA) 优化了ADRGAN参数.
主要成果:
- 在BraTS数据集上实现了99.85%的准确性,99.82%的灵敏性和98.92%的特异性.
- 与现有的脑瘤检测方法相比,其表现优越.
- 报告了13秒的处理运行时间.
结论:
- 拟议的ADRGAN-WHOA-BTD方法为大脑瘤检测提供了高准确性和效率.
- 整合WHOA显著提高了ADRGAN分类器的性能.
- 这种方法显示出在神经成像中临床应用的巨大潜力.
相关概念视频
01:31Classification of Systems-II
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
01:29Aggregates Classification
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...