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efficientnet b3 scales  (Kaggle Inc)

 
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    Kaggle Inc efficientnet b3 scales
    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.
    Efficientnet B3 Scales, 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/product/efficientnet+b3/bio_rxiv__64898__2026__03__19__713075-74-3-15?v=Kaggle+Inc
    Average 86 stars, based on 1 article reviews
    efficientnet b3 scales - by Bioz Stars, 2026-07
    86/100 stars

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    1) Product Images from "A graph-based deep learning framework for diabetic retinopathy classification with topology-aware feature augmentation"

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

    Journal: bioRxiv

    doi: 10.64898/2026.03.19.713075

    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.
    Figure Legend 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.

    Techniques Used:



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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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    Image Search Results


    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:

    Comparative Study of Related State of Art.

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    Article Title: SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare

    doi: 10.3390/diagnostics15010064

    Figure Lengend Snippet: Comparative Study of Related State of Art.

    Article Snippet: Modified EfficientNet-B3 with Deep Transfer Learning , 90.6% , Kaggle skin cancer dataset , [ ] .

    Techniques: Modification, Imaging, Diagnostic Assay