医学影像中多标签视网膜疾病分类的复杂集成深度学习方法

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Asghar Amir, Tariqullah Jan, Mohammad Haseeb Zafar, Shadan Khan Khattak
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引用次数: 0

摘要

本文介绍了一种基于集成深度学习(DL)的多标签视网膜疾病分类(MLRDC)系统,该系统以其高精度和高效率而闻名。利用堆叠集成方法,整合DenseNet201、EfficientNetB4、EfficientNetB3和EfficientNetV2S模型,在视网膜疾病分类方面取得了优异的成绩。采用深度学习作为元模型的MLRDC模型优于单个碱基检测器,其中DenseNet201和EfficientNetV2S的准确率为96.5%,精密度为98.6%,召回率为97.1%,F1得分为97.8%。综上加权多标签分类器的平均准确率为90.6%,精密度为98.3%,召回率为91.2%,F1分数为94.6%,而未加权模型的平均准确率为90%,精密度为98.6%,召回率为93.1%,F1分数为95.7%。采用Logistic回归(LR)作为元模型,该系统的准确率为93.5%,精密度为98.2%,召回率为93.9%,F1分数为96%,最小损失为0.029。这些结果突出了所提出的模型优于基准的最先进的集成,强调了其在医学图像分类中的实际适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Sophisticated Ensemble Deep Learning Approaches for Multilabel Retinal Disease Classification in Medical Imaging

Sophisticated Ensemble Deep Learning Approaches for Multilabel Retinal Disease Classification in Medical Imaging

Sophisticated Ensemble Deep Learning Approaches for Multilabel Retinal Disease Classification in Medical Imaging

Sophisticated Ensemble Deep Learning Approaches for Multilabel Retinal Disease Classification in Medical Imaging

This paper introduces a novel ensemble Deep learning (DL)-based Multi-Label Retinal Disease Classification (MLRDC) system, known for its high accuracy and efficiency. Utilising a stacking ensemble approach, and integrating DenseNet201, EfficientNetB4, EfficientNetB3 and EfficientNetV2S models, exceptional performance in retinal disease classification is achieved. The proposed MLRDC model, leveraging DL as the meta-model, outperforms individual base detectors, with DenseNet201 and EfficientNetV2S achieving an accuracy of 96.5%, precision of 98.6%, recall of 97.1%, and F1 score of 97.8%. Weighted multilabel classifiers in the ensemble exhibit an average accuracy of 90.6%, precision of 98.3%, recall of 91.2%, and F1 score of 94.6%, whereas unweighted models achieve an average accuracy of 90%, precision of 98.6%, recall of 93.1%, and F1 score of 95.7%. Employing Logistic Regression (LR) as the meta-model, the proposed MLRDC system achieves an accuracy of 93.5%, precision of 98.2%, recall of 93.9%, and F1 score of 96%, with a minimal loss of 0.029. These results highlight the superiority of the proposed model over benchmark state-of-the-art ensembles, emphasising its practical applicability in medical image classification.

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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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