基于MRI的Coati优化算法用于基于MRI的脑瘤识别,利用阶段感知复合深度神经网络
Rajesh Kumar Thangavel1, Antony Allwyn Sundarraj2, Jayabrabu Ramakrishnan3
1Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Sri Krishna College of Technology, Coimbatore, Tamil Nadu, India.
Electromagnetic biology and medicine
|January 21, 2025
概括
一个新的以Coati优化算法 (PACDNN-COA-BTI-MRI) 优化的阶段感知复合深度神经网络,可以从MRI扫描中改善大脑瘤的识别. 这种先进的方法提高了准确性和回忆力,优于现有技术,以获得更好的诊断结果.
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:
- 大脑瘤破坏正常的大脑功能,导致感觉,运动和认知缺陷.
- 恶性瘤生长迅速并侵入周围组织,而良性瘤生长缓慢.
- 准确和早期发现脑瘤对于有效的治疗计划至关重要.
研究的目的:
- 引入一种新的深度学习模型,PACDNN-COA-BTI-MRI,用于使用MRI增强脑瘤识别.
- 对现有的最先进的方法来评估拟议模型的性能.
- 为了提高自动脑瘤检测的准确性和可靠性.
主要方法:
- 利用脑瘤数据集进行磁共振成像 (MRI) 分析.
- 使用多变量快速代过 (MFIF) 预处理图像以减少过拟合.
- 使用自主监督非线性转换 (SSNT) 提取基本特征 (模型,形状,强度).
- 在MATLAB中实现了以Coati优化算法 (PACDNN-COA-BTI-MRI) 优化的阶段感知复合深度神经网络.
主要成果:
- 在PACDNN-COA-BTI-MRI模型中,性能指标显著改善.
- 与现有方法相比,实现了更高的精度 (16.7-30.5%),回忆 (19.9-30.1%) 和精度 (16.7-30.8%).
- 在关键绩效指标上表现优于MRI-DLA-ECBT,MRI-BTD-CDMLT和MRI-BTID-CNN技术.
结论:
- PACDNN-COA-BTI-MRI方法提供了一种优越的方法,用于从MRI数据中识别脑瘤.
- 拟议的模型显示了改善诊断准确性和患者结果的潜力.
- 这项研究强调了将先进的深度学习算法与优化的特征提取集成为医疗图像分析的有效性.
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