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DTDO:驾驶培训开发优化启用深度学习方法用于使用MRI进行脑瘤分类

Vadamodula Prasad¹, Issac Diana Jeba Jingle², Gopalsamy Venkadakrishnan Sriramakrishnan³

¹Department of Computer Science & Engineering, Lendi Institute of Engineering & Technology, Jonnada, India.

Network (Bristol, England)

|May 27, 2024

概括

这项研究介绍了DTDO-ZFNet,这是一种用于在MRI扫描中检测脑瘤的新方法. 新方法显著提高了检测准确度,减少了假阴性,有助于更早的诊断.

科学领域:

医疗成像医学成像
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

由于大小,形状和边界不规则的变化,大脑瘤存在检测挑战.
准确的细分和分类对于有效的脑瘤诊断和治疗计划至关重要.

研究的目的:

引入和评估DTDO-ZFNet模型,以使用MRI增强脑瘤检测.
为了解决瘤划分的复杂性,并提高诊断准确性.

主要方法:

磁共振成像 (MRI) 数据的预处理.
使用SegNet进行瘤细分,并使用DTDO (DTBO和CDDO的混合体) 进行优化.
包括GIST,PCA-NGIST,统计,Haralick,SLBT和CNN特征在内的特征提取.
使用经过DTDO培训的ZFNet进行瘤分类.

主要成果:

该DTDO-ZFNet实现了0.944.4的高精度.
证明了0.936的正预测值 (PPV) 和0.939.93的真正率 (TPR).
实现了0.937的负预测值 (NPV),低假负率 (FNR) 为0.061%.

结论:

拟议的DTDO-ZFNet模型为通过MRI扫描检测脑瘤提供了强大而准确的解决方案.
该方法有效地克服了与不规则的瘤边界和变异相关的挑战.
与现有方法相比,DTDO-ZFNet显示出更高的性能,为改善临床诊断铺平了道路.

关键词:

一个大脑瘤.儿童绘画发展优化 (CDDO)驾驶培训发展优化 (DTDO) 项目磁共振成像 (MRI) 是一种磁共振成像技术.

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