在超声波图像中进行甲状腺结节分类的复杂值空间时间图形卷积神经网络优化
Kavin Kumar K1, Rayavel P2, Nithya M3
1Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu, India.
Ultrasonic imaging
|August 25, 2025
概括
这项研究引入了一种新的AI模型,用于在超声波图像中对甲状腺结节进行分类,从而提高诊断的准确性. 这种先进的方法有助于早期发现恶性结节,
科学领域:
- 医学成像
- 人工智能
- 内分泌学
背景情况:
- 甲状腺疾病对新陈代谢有很大影响,
- 准确的甲状腺结节分类对于有效的患者治疗至关重要.
- 现有的诊断方法需要改进以提高精度.
研究的目的:
- 在超声波图像中开发先进的甲状腺结节分类人工智能模型.
- 改善早期发现恶性甲状腺结节.
- 减少错误分类率并帮助临床决策.
主要方法:
- 使用复杂值的空间时间图形卷积神经网络 (CSGCNN) 优化了长鹿优化算法 (GKOA).
- 使用双线双序过器 (BDOF) 进行图像预处理和深度自适应模糊集群 (DAFC) 进行兴趣区域 (RoI) 细分.
- 使用多目标匹配的同步挤压形变形 (MMSSCT) 提取几何和形态特征.
主要成果:
- 拟议的CSGCNN-GKOA-TNC-UI模型与现有方法相比显示出更高的性能.
- 在甲状腺结节分类的f-score和准确性方面取得了显著的改善.
- 该模型在区分良性和恶性结节方面显示出更高的准确性.
结论:
- CSGCNN-GKOA-TNC-UI模型在甲状腺结节分类方面提供了有前途的进展.
- 这种方法通过提高诊断准确度来帮助放射科医生和内分泌医生.
- 早期发现恶性瘤和减少不必要的活检是这种人工智能模型的关键好处.
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