Fig. 3 | Scientific Reports

Fig. 3

From: Enhancing pathological feature discrimination in diabetic retinopathy multi-classification with self-paced progressive multi-scale training

Fig. 3

This figure comprehensively illustrates the architectural design of GPMKLE-Net, a network specifically designed for detecting diabetic retinopathy (DR). It showcases the overall structure, featuring several key components: the Diabetic Retinopathy Attention Residual (DRAR) Block for feature extraction, various stages of the Diabetic Retinopathy Network Backbone (DR-STAGE), the Guided Diabetic Retinopathy Encoder (GDR-Encoder), the multi-scale feature fusion layer, and the ensemble learning classification module with Kullback-Leibler (KL) divergence regularization. The input image passes through the network backbone, where it captures and emphasizes lesion features at multiple scales across different stages. These features are then re-encoded by the GDR-Encoder. Subsequently, through feature fusion and an ensemble learning classification algorithm enhanced with KL divergence regularization, features extracted in previous stages are effectively integrated to determine the DR grade of the input image.

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