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自动化脑瘤诊断:通过基于深度学习的MRI图像分析来增强神经瘤学的能力

Subathra Gunasekaran¹, Prabin Selvestar Mercy Bai², Sandeep Kumar Mathivanan³

¹Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India.

|August 27, 2024

概括

这项研究引入了一种混合深度学习模型,ConvNet-ResNeXt101,用于精确的脑瘤细分和MRI扫描的分类. 这种新的方法实现了高精度,改善了对脑瘤的早期检测和治疗计划.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算生物学计算生物学

背景情况:

大脑瘤对健康构成重大挑战,需要及早检测才能有效治疗.
磁共振成像 (MRI) 对于脑瘤诊断至关重要,但由于瘤的复杂性,准确的细分很难.
精确的瘤细分对于治疗计划和预后至关重要.

研究的目的:

开发一种新的混合深度学习技术,用于自动化脑瘤细分和分类.
用MRI数据提高脑瘤分析的准确性和效率.

主要方法:

利用BRATS 2020数据集进行MRI图像和瘤细分.
采用批量规范化和AlexNet进行特征提取.
应用高级鱼优化 (AWO) 为最佳的特征选择.
实现了一个混合的ConvNet-ResNeXt101模型用于细分和分类.

主要成果:

ConvNet-ResNeXt101模型在瘤核心细分方面实现了99.27%的准确性.
与现有方法相比,表现出优越的性能.
实现了0.53秒的最低学习时间.

结论:

拟议的ConvNet-ResNeXt101混合深度学习模型为大脑瘤细分和分类提供了高度准确和高效的解决方案.
这种技术有可能显著提高早期脑瘤检测和治疗计划.

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