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EfficientNet scaling for retinal disease classification shows task-dependent effects

By PulseAugur Editorial · [1 sources] ·

A study published on arXiv investigated the effectiveness of scaling EfficientNet models for classifying retinal diseases from spectral-domain optical coherence tomography (SD-OCT) images. Researchers found that larger EfficientNet variants did not consistently improve classification accuracy, with EfficientNet-B1 achieving the highest accuracy and F1 score for Drusen classification. The study highlights that the optimal model size for this medical imaging task is dependent on the specific protocol and task, differing from the performance hierarchy observed in natural image classification on ImageNet. AI

IMPACT Suggests that transfer learning from natural image models to medical imaging requires careful consideration of model scaling, as performance gains are not always linear.

RANK_REASON Academic paper detailing model performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

EfficientNet scaling for retinal disease classification shows task-dependent effects

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Academic paper detailing model performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shkelqim Sherifi ·

    Within-Family EfficientNet Scaling under Fixed-Resolution Transfer Learning for Spectral-Domain Optical Coherence Tomography Retinal Disease Classification

    arXiv:2611.06452v1 Announce Type: cross Abstract: Background/Objectives: CNNs perform well in retinal disease classification from spectral-domain optical coherence tomography (SD-OCT), but whether EfficientNet's natural-image scaling hierarchy transfers to medical-image transfer …

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