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HIDANet: a lightweight deep learning framework for Vannamei post-larval stage classification and morphometric estimation with background bias validation

IntroductionQuality control of hatchery production relies on accurate developmental staging of the Pacific white shrimp Litopenaeus vannamei post-larvae (PL), but current methods rely on subjective manual visual evaluation that leads to observer bias and inconsistency.MethodsIn this study, the Hierarchical Isotropic Dense Attention Network (HIDANet) has been introduced, a lightweight convolutional neural network with 0.033M parameters that learns to classify seven post-larval stages (PL5–PL12) i

Record details

Published: 23 July 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Bias & fairness
Retrieved: 25 July 2026

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How to cite this record

ethics.ai (23 July 2026), “HIDANet: a lightweight deep learning framework for Vannamei post-larval stage classification and morphometric estimation with background bias validation,” evidence record 13187, https://ethics.ai/record/13187 (originally published by Frontiers in Artificial Intelligence).

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