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efficientnet  (Kaggle Inc)

 
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    Kaggle Inc efficientnet
    Efficientnet, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/efficientnet/product/Kaggle Inc
    Average 86 stars, based on 1 article reviews
    efficientnet - by Bioz Stars, 2026-06
    86/100 stars

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    Overview of the proposed pipeline. Each fundus image is preprocessed to produce a vessel-enhanced image and a morphological skeleton for persistent <t>homology.</t> <t>EfficientNet-B3</t> provides 1536-d CNN features; TDA yields six topological descriptors concatenated to form augmented node representations. A topology-aware population graph connects similar images; two-layer GraphSAGE refines representations through neighbourhood aggregation for five-class DR grading.
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    Overview of the proposed pipeline. Each fundus image is preprocessed to produce a vessel-enhanced image and a morphological skeleton for persistent homology. EfficientNet-B3 provides 1536-d CNN features; TDA yields six topological descriptors concatenated to form augmented node representations. A topology-aware population graph connects similar images; two-layer GraphSAGE refines representations through neighbourhood aggregation for five-class DR grading.

    Journal: bioRxiv

    Article Title: A graph-based deep learning framework for diabetic retinopathy classification with topology-aware feature augmentation

    doi: 10.64898/2026.03.19.713075

    Figure Lengend Snippet: Overview of the proposed pipeline. Each fundus image is preprocessed to produce a vessel-enhanced image and a morphological skeleton for persistent homology. EfficientNet-B3 provides 1536-d CNN features; TDA yields six topological descriptors concatenated to form augmented node representations. A topology-aware population graph connects similar images; two-layer GraphSAGE refines representations through neighbourhood aggregation for five-class DR grading.

    Article Snippet: Feature extraction with EfficientNet-B3 scales linearly as O ( N ); for N = 88,702 (Kaggle DR), this requires ≈7 min on a single A100 GPU.

    Techniques: