使用定制转移学习算法对牛皮的分类取得了进展
L Lakshmi1, K Dhana Sree Devi2, KongaraSrinivasa Rao3
1Department of Artificial Intelligence & Data Science, Faculty of Science and Technology (IcfaiTech), ICFAI Foundation for Higher Education, Hyderabad, 501203, India.
Scientific reports
|February 2, 2026
概括
这项研究引入了人工智能方法来分类牛皮类型,使用InceptionV3等深度学习模型来实现高准确度. 这为广泛的皮肤疾病 - - 牛皮 - - 提供了先进的诊断工具.
科学领域:
- 皮肤病学 皮肤病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 牛皮影响全球人口的2-3%,影响生活质量,增加伴随疾病的风险.
- 目前的牛皮亚型的诊断方法由于人为错误而缺乏一致的准确性.
- 精确的牛皮的分类对于有效的治疗和管理至关重要.
研究的目的:
- 开发一种自动化系统,使用深度学习对七种类型的牛皮进行分类.
- 从公开可用的来源创建一个新的多类牛皮数据集.
- 通过图像增强来解决皮肤病学数据集中的类失衡问题.
主要方法:
- 使用了公开可用的数据集:皮肤损伤,ISIC和DEMANET.
- 采用图像增强技术来平衡数据集类.
- 通过深度学习模型实施转移学习:ResNet50,InceptionResNetV2和InceptionV3.
- 使用Adam和RMSprop优化器训练和验证模型.
主要成果:
- InceptionV3实现了最高的准确性 (99.57%的培训,96.82%的验证,98.68%的测试).
- InceptionV2也表现出强的表现 (99.07%的培训,96.65%的验证,97.20%的测试).
- ResNet50的准确率很好,虽然相对较低 (92.36%的培训,84.59%的验证,83.55%的测试).
结论:
- 深度学习模型,特别是InceptionV3,显示出对精确的牛皮分类有很大的希望.
- 开发的数据集和方法提供了一个强大的工具,用于自动化牛皮的诊断.
- 这种人工智能驱动的方法可能会提高诊断准确度,克服传统方法的局限性.
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