在眼病诊断中的多模式融合策略
Sara El-Ateif1, Ali Idri2,3
1Software Project Management Research Team, ENSIAS, Mohammed V University, BP 713, Agdal, Rabat, Morocco.
Journal of imaging informatics in medicine
|April 19, 2024
概括
多式眼镜成像数据的晚期融合显著提高了糖尿病眼病的诊断准确性. 这种使用ResNet50V2的方法实现了100%的准确性,优于早期和联合聚变方法.
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:
- 糖尿病性眼部疾病对视力损失和失明的风险很大.
- 通过单一的成像方式来诊断这些疾病是具有挑战性的.
- 多模式融合为提高诊断准确性提供了一个有前途的方法.
研究的目的:
- 评估三种多模式融合策略 (早期,联合和晚期) 的有效性,用于自动检测眼部疾病.
- 在这些融合策略中比较不同卷积神经网络模型的性能.
- 通过多式联网膜成像来确定最佳的融合策略来诊断眼睛疾病.
主要方法:
- 使用了三个数据集: fundus fluorescein血管学,斑点成像和结合数字视网膜图像.
- 采用了最先进的卷积神经网络模型,包括DenseNet121,InceptionResNetV2和ResNet50V2.2.
- 实施并比较早期,联合和晚期融合战略,以整合多式联运数据.
主要成果:
- 与DenseNet121的早期融合 (类型0) 实现了99.45%的平均精度.
- 与InceptionResNetV2的联合融合达到99.58%的平均精度.
- 与ResNet50V2的晚期融合在所有指标上实现了完美的100%准确性,超过了其他方法并匹配了最先进的模型.
结论:
- 与早期和关节融合相比,晚期融合是诊断眼病的最佳策略.
- 通过晚期融合的多式联网数据集成显著提高了诊断性能.
- 这些发现突出了与ResNet50V2的晚期融合的潜力,用于准确的自动眼病检测.
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