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使用深度学习的自动智能脑瘤分类和预测系统.

Qurat Ul Ain Ishfaq1, Rozi Bibi1, Abid Ali2,3

  • 1Department of Computer Science, GPGC(W), Haripur, Pakistan.

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|April 28, 2025
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概括
此摘要是机器生成的。

这项研究引入了一个使用深度学习进行早期脑瘤检测和分类的智能监测系统. 该系统实现了高精度,模型在MRI扫描中分类脑瘤方面达到高达99.76%.

关键词:
大脑瘤是什么?在美国,CNN是CNN.深度学习是一种深度学习.有效的-b4 有效的-b4在 Inception-v4 中使用.智能医疗保健是一个智能医疗保健.

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科学领域:

  • 医学成像分析分析 医学成像分析
  • 医疗保健中的人工智能
  • 神经学 神经学

背景情况:

  • 大脑瘤是一个严重的健康问题,其症状重叠于其他神经疾病,往往会推迟诊断.
  • 及时诊断对于有效治疗,改善患者的治疗结果和选择适当的治疗方法至关重要.
  • 早期发现脑瘤可以预防晚期,减少并发症,提高恢复率.

研究的目的:

  • 为早期及时检测,分类和预测脑瘤提出一个智能监测系统.
  • 用MRI数据集开发和评估用于脑瘤分类的深度学习模型.
  • 评估定制CNN,Inception-v4和EfficientNet-B4模型在识别10种脑瘤类别中的性能.

主要方法:

  • 开发了一个定制的卷积神经网络 (CNN) 模型,以提高脑瘤分类的计算效率和适应性.
  • 两个预先训练的模型Inception-v4和EfficientNet-B4与自定义的CNN一起用于分类脑瘤病例.
  • 这些模型在各种脑部MRI数据集上进行训练和评估,使用准确度,精度,灵敏度和特异性等指标.

主要成果:

  • 定制的CNN模型实现了97.58%的平均分类准确率.
  • 预先训练的模型表现出卓越的性能,Inception-v4达到99.56%的平均精度,EfficientNet-B4达到99.76%的平均精度.
  • 在1000张图像的测试数据集上,模型预测了96.5% (CNN),99.3% (Inception-v4) 和99.7% (EfficientNet-B4) 的准确性,表明部署后的持续性能.

结论:

  • 拟议的智能监控系统有效地利用深度学习来准确检测和分类脑瘤.
  • Inception-v4和EfficientNet-B4模型显示出在脑瘤诊断中实际应用的巨大潜力.
  • 这项研究强调了先进的人工智能技术在改善脑瘤早期检测和患者管理方面的重要性.