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I-BrainNet:基于深度学习和物联网 (DL/IoT) 的框架,用于大脑瘤的分类

Abdullahi Umar Ibrahim^1,2, Glodie Mpia Engo³, Ibrahim Ame^3,4

¹Department of Biomedical Engineering, Near East University, Mersin 10, Nicosia, Turkey. Abdullahi.umaribrahim@neu.edu.tr.

Journal of imaging informatics in medicine

|March 10, 2025

概括

深度学习模型准确地使用MRI和CT扫描对脑瘤进行分类. 预训练的MobileNetV2实现了99%以上的准确性,改善了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
在瘤学瘤学.

背景情况:

大脑瘤是癌症死亡的主要原因,原因是诊断的挑战.
目前的活检和核磁共振等方法有其局限性,包括不准确性和专家依赖性.
准确的诊断和分期对于有效的脑瘤治疗至关重要.

研究的目的:

应用深度学习 (DL) 来分类MRI与非MRI扫描.
开发用于瘤与没有瘤检测的DL模型.
为了提高脑瘤诊断的准确性和效率.

主要方法:

利用一个多式数据集,包括9616张脑瘤图像 (CT和MRI).
使用四个DL网络进行应用图像预处理,数据增强和模型训练:MobileNetV2,ResNet,Inceptionv3和VGG16.
开发了一个集成的DL/IoT框架 (I-BrainNet) 用于实时分类.

主要成果:

预先训练的MobileNetV2表现出卓越的性能.
在MRI扫描与非MRI扫描的区分方面获得了99.94%的准确性.
在区分瘤与无瘤病例方面达到99.00%的准确性.

结论:

深度学习技术,特别是MobileNetV2,提供高度准确的脑瘤分类.
开发的I-BrainNet框架使实时,精确的脑瘤诊断成为可能.
这种方法有可能显著改善患者的发病率和死亡率.

关键词:

大脑瘤是什么?深度学习 (Deep Learning) 是一种深度学习.诊断诊断诊断诊断诊断物联网的物联网,就是物联网.这就是为什么MRI是MRI.医学成像医学成像

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