基于超声波的结石分类使用克罗纳克的自我组织地图向前波网络
Pendela Kanchanamala1, Kishore Bhamidipati2, Rohini Arunachalam3
1Department of CSE-AI & DS, GMR Institute of Technology, Rajam, Andhra Pradesh, India.
Ultrasonic imaging
|January 30, 2026
概括
一个新的深度学习模型,克罗尼克自组织地图前向波网络 (KSOMFHNet),准确地从超声波图像中分类结石. 这种自动化方法提高了诊断效率和尿道疾病检测的准确性.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 结石疾病是一种常见的泌尿病状况,有可能导致严重的并发症.
- 结石的传统超声波诊断依赖于手动解释,在准确性和效率方面面临局限性.
- 需要自动化诊断工具来克服主观解释,提高及时检测.
研究的目的:
- 开发和评估一种新的深度学习模型,用于使用超声波成像进行自动结石分类.
- 与传统方法相比,提高诊断准确性和效率.
主要方法:
- 开发了一个深度学习模型,克罗尼克自组织地图前向波网络 (KSOMFHNet).
- 图像预处理涉及双双边过以消除噪音.
- 用深度递归残余网络 (DRRN) 进行脏细分.
- 特征提取结合了BRIEF,形状和GLCM描述符.
- 分类使用了混合DKN和SOMNet架构.
主要成果:
- 在培训数据方面,KSOMFHNet实现了高绩效指标.
- 准确率: 91.984% 的准确率.
- 真正的阳性率 (TPR):90.543%
- 真正阴性率 (TNR): 92.248% 的确阴性率 (TNR): 92.248% 的确阴性率
- 精度:90.179% 的精度.
- F1分数:90.360% 在F1分数中
结论:
- 拟议的KSOMFHNet显示了准确和高效的结石分类的巨大潜力.
- 自动化深度学习方法为临床环境中手动解释提供了一个有希望的替代方案.
- 对于广泛的临床部署,建议进一步验证.
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