一个两阶段的深度学习框架,用于检测病,使用修改的无镜像成像和EfficientNetB2 .
Noha A El-Hag1, Walid El-Shafai2,3, Hayam A Abd El-Hameed4
1The Higher Institute of Commercial Sciences, Al mahalla Al kubra, Algarbia, 31951, Egypt.
Scientific reports
|February 11, 2026
概括
一个新的两阶段诊断模型显著改善了病检测. 使用修改的无镜技术和EfficientNet-B2,它在识别病理方面达到98.27%的准确性.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 腎臟病學 (nephrology) 是一種醫學專業.
背景情况:
- 脏疾病是一个日益增长的公共卫生挑战,其发病率越来越高.
- 准确和早期诊断病态对于有效的患者管理至关重要.
- 现有的诊断方法在图像质量和分类准确性方面可能存在局限性.
研究的目的:
- 开发和验证一种新的两阶段诊断模型,用于更好地检测脏疾病.
- 用先进的增强技术提高图像的视觉质量.
- 为了在分类各种病理方面实现高精度.
主要方法:
- 修改无镜子 (MSF) 技术的实施,用于适应性图像增强.
- 使用EfficientNet-B2深度学习架构对增强图像进行分类.
- 与已建立的预训练模型进行比较分析 (VGG16,ResNet50,DenseNet,EfficientNet变体).
主要成果:
- 拟议的模型实现了98.27%的诊断准确度.
- 该模型与所有基准预训练模型相比,表现优越.
- 在正常脏状况和瘤,结石和囊等病理之间实现了有效的区分.
结论:
- 综合的MSF技术和EfficientNet-B2模型为准确的病诊断提供了强大的方法.
- 这项研究强调了先进的图像增强以及医疗成像深度学习的潜力.
- 开发的模型提供了一个可扩展的解决方案,用于改善复杂的医学成像场景中的诊断准确性.
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