失衡青光眼检测的权重损失:从视觉解释的洞察力.
Devin Jaya Nugraha1, Novanto Yudistira1, Agus Wahyu Widodo1
1Informatics Engineering, Faculty of Computer Science, Brawijaya University, Jalan Veteran 8, Malang, 65145, East Java, Indonesia.
Computers in biology and medicine
|August 18, 2025
概括
这项研究通过使用卷积神经网络 (CNN) 的加权损失函数,提高了在 fundus 图像中的青光眼的检测. 该方法显著改善了回忆和AUC,有助于早期诊断这种视力威胁的疾病.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 玻璃眼是由于视神经损伤导致不可逆转的视力丧失的主要原因.
- 早期的青光眼检测至关重要,但由于 fundus 图像数据集的不平衡而受到阻碍.
- 阶级不平衡对医学诊断中的机器学习模型构成了重大挑战.
研究的目的:
- 提高卷积神经网络 (CNN) 的性能和可解释性,以检测青光眼.
- 为了应对格洛科马底部图像数据集中的阶级不平衡的挑战.
- 为了评估加权损失函数在提高CNN性能方面的有效性.
主要方法:
- 将加权损失函数应用于CNNs以检测青光眼.
- 使用了标准化的SMDG-19数据集,包括来自19个公共来源的数据.
- 通过回忆,F1得分,精度,准确性和AUC来评估性能.
- 使用Grad-CAM进行解释性分析.
主要成果:
- 回忆率提高了44.75% (从60.3%增加到87.3%),F1得分增加了7.25% (从66.5%增加到71.4%).
- 曲线下的面积 (AUC) 增加了3.21% (从84.2%到87.4%).
- 观察到精度 (-6.53%) 和准确度 (-4.10%) 的轻微下降.
- 格拉德-CAM可视化证实了对相关视神经头部区域的关注.
结论:
- 权重损失函数有效地提高了眼的检测CNN性能,特别是改善了回忆和AUC.
- 该战略通过专注于临床重要领域,提高了模型的可解释性.
- 这种方法为克服眼科图像分析中的阶级不平衡提供了有希望的解决方案.
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