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通过使用卷积神经网络的磁共振成像检测大脑瘤

Irene Rethemiotaki¹

¹School of Electrical and Computer Engineering, Technical University of Crete, Chania, Crete, Greece.

Contemporary oncology (Poznan, Poland)

|February 26, 2024

概括

这项研究使用人工智能从MRI图像中分类大脑瘤,MobileNetV2达到99%的准确性. 这项研究强调了人工智能.

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:

脑瘤检测和分类对于有效治疗至关重要.
磁共振成像 (MRI) 是可视化大脑结构的关键方式.
计算智能为医学图像分析提供了先进的工具.

研究的目的:

在MRI图像上使用计算智能技术检测和分类脑瘤.
为了比较12个不同的卷积神经网络 (CNN) 模型用于脑瘤分类的性能.
为了确定最合适的CNN模型来准确诊断脑瘤.

主要方法:

使用了3264张MRI脑图像 (质瘤,脑膜瘤,垂体,健康) 的数据集.
评估了包括谷歌网和MobileNetV2在内的12个CNN模型.
实验涉及图像预处理,超参数调整和使用精度,精度,回忆和F测量的性能评估.

主要成果:

在诊断脑瘤方面,MobileNetV2 CNN模型实现了99%的准确率,98%的回忆率和99%的F1得分.
在被评估的CNN中,GoogleNet在脑瘤分类方面表现出最高的准确性 (97%).
在所有测试的神经网络中,分析了每个瘤类型的性能指标.

结论:

人工智能和机器学习对于推进脑瘤预测至关重要.
这项研究在脑瘤分类方面取得了最先进的准确性.
同时比较多个神经网络为模型选择提供了全面的见解.

关键词:

人工智能的人工智能是人工智能.大脑瘤脑瘤机器学习是机器学习.神经网络的神经网络的神经网络

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