使用图形卷积神经网络架构进行增强的大脑瘤分类
M Ravinder1, Garima Saluja1, Sarah Allabun2
1CSE, Indira Gandhi Delhi Technical University for Women, New Delhi, India.
Scientific reports
|September 11, 2023
概括
一个新的图形神经网络 (GNN) 和卷积神经网络 (CNN) 模型有效地检测和分类脑瘤,达到95.01%的准确性. 这种方法通过分析非欧几里德图像数据来增强早期诊断.
科学领域:
- 医学成像分析 医学成像分析
- 在瘤学中使用人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 大脑瘤对健康构成重大风险,需要早期检测才能有效治疗.
- 传统模型与非欧几里德图像数据和像素近距离分析作斗争.
- 对瘤类型 (脑膜瘤,垂体瘤,质瘤) 的准确分类对于患者的管理至关重要.
研究的目的:
- 开发一种用于准确检测和分类脑瘤的新型模型.
- 解决传统模型在处理非欧几里德图像数据方面的局限性.
- 使用混合图形神经网络 (GNN) 和卷积神经网络 (CNN) 方法来提高诊断准确性.
主要方法:
- 开发了一个基于卷积神经网络 (CNN) 的图形神经网络 (GNN) 模型.
- 该模型使用图形卷积运算来修改节点特征,通过结合邻近节点的信息来修改节点特征.
- 与批量规范化和丢弃层集成的26层CNN处理了基于图形的输入.
主要成果:
- 拟议的基于图形的卷积神经网络 (GCNN) 模型成功地解决了图像数据中的非欧几里德距离考虑.
- 评估了五种网络变体 (Net-0至Net-4),其中Net-2表现出卓越的性能.
- 网2模型在脑瘤检测和分类方面实现了最高准确率95.01%.
结论:
- 拟议的混合GNN-CNN模型在脑瘤检测和分类方面取得了重大进展.
- 这种新的技术为疑似脑瘤病例的统计检测提供了关键的替代方案.
- 该模型在处理复杂图像数据方面的有效性表明,该模型可能具有更广泛的临床应用.
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