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低资源的联合学习用于指甲病的分类,通过部署交叉和异质的数据集分布

Vikas Khullar¹, Mohamed Abbas^2,3, Isha Kansal¹

¹Department of CSE, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.

Scientific reports

|February 7, 2026

概括

本研究引入了一种机器学习系统,用于使用联合学习检测指甲病,在没有数据共享的情况下实现双向LSTM模型的91.8%准确性. 联合学习模型的准确性超过99%,确保数据隐私.

科学领域:

皮肤病学和医学成像学
医疗保健中的人工智能
机器学习用于疾病诊断和诊断

背景情况:

爪子疾病,从常见的真菌感染到黑色素瘤,可以表明潜在的健康问题,需要准确的诊断.
当前的诊断方法可能缺乏效率或在处理敏感患者数据时引发隐私问题.

研究的目的:

开发使用先进机器学习技术的指甲疾病检测系统,特别是转移学习和联合学习.
证明结合机器学习和联合学习的有效性,以高精度检测指甲疾病,同时保持数据隐私.

主要方法:

在统一的质量检查后,利用了各种指甲状况图像 (例如,形形黑色素瘤,形形,刺) 进行模型培训.
采用特征提取模型 (ResNet152V2,DenseNet201,MobileNetV2,InceptionResNetV2) 和线性差异分析 (LDA) 来进行维度缩小.
比较分类模型包括深度神经网络 (DNN),长期短期记忆 (LSTM) 和双向LSTM (Bi-LSTM),结合联合学习来维护隐私的分布式培训.

主要成果:

双向LSTM模型在检测指甲疾病方面实现了最高的分类准确率91.8%.
联合学习策略使客户能够共同培训深度学习模型,保持数据隐私.
基于学习的联合模型在均随机和结构化数据分布中实现了超过99%的验证准确率.

结论:

拟议的联合学习方法有效地以高准确度检测指甲疾病,同时确保患者数据的隐私.
机器学习,特别是联合学习,为皮肤病学中开发强大且保护隐私的诊断工具提供了有希望的途径.
该系统在不同数据分布上的高性能突出显示了其在现实世界临床应用中的潜力.

关键词:

有效的网络有效的网络功能提取功能提取联合学习是联合学习.线性差异分析 (LDA) 是指线性差异分析.爪病是指甲疾病的一种疾病.神经网络分类神经网络分类

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