微调的深度转移学习:为准确的慢性病分类提供一个有效的策略
Zeshan Aslam Khan1,2, Muhammad Waqar2, Hashir Ullah Khan2
1Electrical and Computer Engineering, International Islamic University, Islamabad, Pakistan.
PeerJ. Computer science
|June 26, 2025
概括
本研究引入了使用EfficientNetV2B0的深度转移学习方法,用于准确的慢性病分类. 微调的模型达到99.75%的准确性,为早期诊断提供了高效的解决方案.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 脏疾病是一个全球性的健康挑战,需要早期诊断才能有效管理.
- 深度学习模型在医学诊断方面表现有前途,但往往面临复杂性和资源限制的挑战.
- 准确的慢性病分类对于及时干预和患者的治疗结果至关重要.
研究的目的:
- 开发一个准确和计算效率高的深度转移学习模型,用于慢性病的分类.
- 探索ConvNeXt和EfficientNetV2的新型变体,以提高诊断性能.
- 为在边缘设备上实时部署创建一个具有成本效益的解决方案.
主要方法:
- 使用基于计算机断层扫描 (CT) 的基准脏数据集,共12446次扫描.
- 使用微调的ConvNeXt和EfficientNetV2模型进行深度转移学习.
- 应用数据修剪来解决类不平衡和全面的超参数调整.
主要成果:
- 微调的EfficientNetV2B0模型在平衡的数据集上实现了99.75%的通用测试准确度.
- 该模型显示了高精度 (99.75%),回忆 (99.63%) 和F1得分 (99.75%).
- 在原始,不平衡的CT脏数据集上实现了99.73%的准确性,展示了可扩展性.
结论:
- 通过精心调整的EfficientNetV2B0进行深度转移学习,为慢性病诊断提供了高度准确和高效的方法.
- 拟议的模型在计算上便宜,适合在医疗或移动边缘设备上实时部署.
- 这种方法在病分类的准确性和效率方面优于现有的方法.
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