Research Article
Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy
Table 1
Summary of the prediction performance of different transfer learning models in predicting diabetic retinopathy.
| | Trained on Kaggle training set | Trained on T1D training set | Tested on T1D testing set | Tested on Kaggle testing set | Tested on T1D testing set | Tested on Kaggle testing set | AUC | SEN | SPE | AUC | SEN | SPE | AUC | SEN | SPE | AUC | SEN | SPE |
| DenseNet-121 | 0.86 | 0.77 | 0.79 | 0.74 | 0.67 | 0.71 | 0.91 | 0.81 | 0.86 | 0.55 | 0.55 | 0.54 | InceptionV3 | 0.86 | 0.74 | 0.79 | 0.74 | 0.62 | 0.74 | 0.87 | 0.73 | 0.86 | 0.59 | 0.56 | 0.59 | VGG16 | 0.88 | 0.78 | 0.82 | 0.77 | 0.66 | 0.75 | 0.84 | 0.67 | 0.84 | 0.54 | 0.59 | 0.49 | Xception | 0.86 | 0.74 | 0.82 | 0.71 | 0.60 | 0.72 | 0.88 | 0.74 | 0.90 | 0.59 | 0.61 | 0.52 |
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T1D: type 1 diabetes; AUC: area under the curve; SEN: sensitivity; SPE: specificity.
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