使用新型修改的受限制的博尔茨曼基于机器的平衡优化器预测人类麻疹疾病
D Devarajan1, P Dhana Lakshmi2, S Krishnaveni3
1Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, 611002, India. devarajand@ymail.com.
Scientific reports
|July 30, 2024
概括
一种新的深度学习方法使用图像分析准确地预测麻疹. 这种方法结合了卷积块注意力模块和修改的受限制的博尔茨曼机器与平衡优化器,在早期疾病检测方面表现出卓越的性能.
科学领域:
- 医疗成像医学成像
- 计算生物学 计算生物学
- 流行病学 流行病学
背景情况:
- 全球卫生格局面临着新兴传染病带来的挑战,包括最近人猿疫情的增加.
- 准确及时诊断麻疹对于预防和控制大流行病至关重要.
- 深度学习在医学图像分析中显示出有前途的可能性,用于预测疾病,包括皮肤疾病.
研究的目的:
- 开发和评估一种新的深度学习方法,用于以图像为导向的预测人类麻疹疾病.
- 评估一种拟议的模型的有效性,该模型集成了卷积块注意力模块 (CBAM) 和经平衡优化器 (EO) 优化的修改限制波尔兹曼机器 (MRBM).
主要方法:
- 使用麻疹皮肤损伤数据集进行培训和验证.
- 应用图像预处理技术,包括大小调整,规范化和数据增强.
- 采用CBAM用于特征提取,并通过EO优化MRBM进行预测,专注于最小化错误.
主要成果:
- 与现有模型相比,拟议的MRBM-EO模型在预测麻疹方面表现优越.
- 在根平均平方误差 (RMSE) 中取得了显著的改进,超过了PSO-SVM,Xception-CBAM-Dense,ShuffleNet和RBM.
- 在疾病预测方面展示了更高的准确性,超过了基准模型.
结论:
- 开发的深度学习模型提供了一个有前途的工具,用于准确有效地预测人类水.
- 整合CBAM和EO优化的MRBM为皮肤病诊断提供了一个强大的框架.
- 这种方法可以帮助早期检测和管理麻疹疫情,为全球卫生安全做出贡献.
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