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面具基于区域的卷积神经网络和VGG-16启发的大脑瘤细分

Niha Kamal Basha¹, Christo Ananth^2,3, K Muthukumaran⁴

¹School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.

Scientific reports

|July 30, 2024

概括

这项研究引入了一种有效的脑瘤检测方法,使用MRI扫描的深度学习. 拟议的模型实现了近99%的准确性和灵敏度,显著提高了诊断能力.

科学领域:

医学成像分析医学成像分析
医疗保健中的人工智能
神经瘤学诊断诊断诊断

背景情况:

脑瘤细分对于准确的诊断和治疗计划至关重要.
磁共振成像 (MRI) 是检测大脑异常的标准.
现有的方法需要改进,以提高瘤定位的精度.

研究的目的:

利用深度学习开发一种有效和高度准确的脑瘤检测方法.
为了提高医疗专业人员的诊断准确度.
利用先进的人工智能技术进行精确的瘤细分和识别.

主要方法:

使用基于区域的卷积神经网络 (R-CNN) 面具进行细分.
雇佣Grad-CAM和转移学习以有效检测瘤.
训练模型使用Inception V3,VGG-16和ResNet-50架构在大脑MRI图像大脑瘤检测数据集上.

主要成果:

基于转移学习的模型表现出高度的灵敏度和准确性.
实现了大约99%的准确度和灵敏度,超过了当前的方法.
用回忆,特异性,敏感性,准确性,精度和F1分数来评估性能.

结论:

拟议的方法在脑瘤检测准确度方面取得了显著的进步.
深度学习模型,特别是VGG-16的影响,显示出临床应用的巨大潜力.
这种方法有助于临床医生进行高度准确的诊断,改善患者的治疗结果.

关键词:

大脑瘤的细分脑瘤的细分这是Grad-CAM.开始 V3 开始 V3 在 R-CNN 面具中,使用 R-CNN 面具.在ResNet50中使用ResNet50 转移学习转移学习在VGG16中,VGG16是VGG16中的一个.

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