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Yuki Wong¹, Eileen Lee Ming Su¹, Che Fai Yeong¹

¹Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.

|May 9, 2025

概括

这项研究介绍了一种人工智能驱动的系统,用于使用深度学习和MRI扫描进行自动脑瘤分类. 该模型实现了99.24%的准确性,改善了早期诊断和患者的治疗结果.

科学领域:

医疗成像医学成像
人工智能的人工智能
计算生物学计算生物学

背景情况:

大脑瘤是一个重大的诊断挑战,需要早期检测和准确的分类.
目前的诊断方法可能耗时,容易出现人为错误.
自动化系统有可能提高脑瘤诊断的准确性和效率.

研究的目的:

开发和评估使用深度学习 (DL) 和磁共振成像 (MRI) 的自动化脑瘤分类系统.
准确检测和分类常见的大脑瘤,包括质瘤,脑膜瘤和垂体瘤,以及正常扫描.
提高诊断准确度,促进早期医疗干预.

主要方法:

使用一个卷积神经网络 (CNN) 架构,VGG16作为基本模型.
在各种公共数据集上采用数据增强技术,共计17,136张大脑MRI图像.
开发了一个用户友好的Web应用程序,用于图像上传和使用HTML和Dash进行瘤预测.

主要成果:

获得了99.24%的分类准确度,超过了现有的基准.
高精度归因于大量多样化的数据集,优化的网络配置,微调和数据增强.
开发的网络应用程序证明了用于快速瘤预测的实际临床实用性.

结论:

这种由人工智能驱动的系统为脑瘤分类提供了高效可靠的解决方案.
这种方法有可能显著减少诊断错误并改善患者护理.
通过及时干预,自动脑瘤检测的这一进步有望改善患者的治疗结果.

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