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Dual-Attention Ensemble Residual Learning for Robust Diabetic Retinopathy Classification under Class Imbalance

Precious Mojolaoluwa Ojo, Chima Wisdom Olumba, Uchechi Joyce Nneji, Happy, Nkanta Monday, Grace Ugochi Nneji, Chibueze Favour Aririguzo, Edwin Sunday, Umana, Gladys Chinyere Olumba

Abstract

Diabetic Retinopathy (DR) is one of the common complications of diabetic patients, which can lead to vision loss or even blindness. Early detection and timely treatment are crucial for preventing vision loss. However, in real-world applications, DR classification tasks often face the challenge of imbalanced data distributions, where one class significantly outnumbers the others. Therefore, this research proposed on a novel ensemble learning model which combines ResNet50 and Dual attention method for Diabetic Retinopathy classification task trained on the pre- processed APTOS 2019 Blindness Detection datasets that were downloaded from Kaggle. The model outperformed the pre-training model with an Average Precision (AP) of 1 in the No DR class and an Area Under the Curve of 0.97 in the Advanced class, 0.93 in the Mild class, 1 in the No DR class, and 0.96 in the Proliferative DR class, respectively. Besides, this paper utilizes local interpretable Model Interpretation as the interpretable AI tool. The integrated model that combines ResNet50 with the dual attention strategy proposed in this research performs admirably in the diabetic retinopathy classification test. It has a high potential for medical auxiliary diagnosis and exhibits strong discriminative ability and good interpretability, particularly under imbalanced data.

Keywords

CNNDiabetic RetinopathyDeep LearningResNet50Dual AttentionXAI

References

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