从MRI使用图像增强和卷积神经网络技术进行脑瘤分类.
Zahid Rasheed1, Yong-Kui Ma1, Inam Ullah2
1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
Brain sciences
|September 28, 2023
概括
这项研究引入了一种先进的深度学习模型,用于使用增强的MRI图像进行准确的脑瘤分类. 这种新方法的准确率超过97%,帮助医生进行精确的诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 通过MRI检测大脑瘤是复杂的,容易出现错误.
- 深度学习 (DL) 为医疗图像分析提供了自动化解决方案.
- 卷积神经网络 (CNN) 在图像分类任务中是有效的.
研究的目的:
- 开发和验证一种新的深度学习方法来分类脑瘤 (质瘤,脑膜瘤,垂体瘤) 和MRI的非瘤病例.
- 整合图像增强技术,以提高分类性能.
- 将拟议的模型与已建立的预训练模型进行比较.
主要方法:
- 实现了一种结合高斯模糊利和CLAHE自适应式直方图等级的新型模型,以增强图像.
- 使用深度学习方法对脑瘤进行分类.
- 使用基准数据集进行验证,并与VGG16,ResNet50,VGG19,InceptionV3和MobileNetV2.2进行比较.
主要成果:
- 拟议的方法实现了97.84%的分类精度.
- 精度,回忆和F1得分超过97.85%,表明性能高.
- 该模型在不同类型的瘤中展示了强大的概括能力.
结论:
- 开发的方法精确地分类了常见的大脑瘤类型,具有高精度.
- 该技术显示出作为医生在脑瘤诊断中的宝贵工具的巨大潜力.
- 将图像增强与DL集成,可以提高诊断准确性和效率.
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