{
  "id": 13187,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1821929",
  "title": "HIDANet: a lightweight deep learning framework for Vannamei post-larval stage classification and morphometric estimation with background bias validation",
  "summary": "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",
  "authors": "Sugunapriya A",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T00:00:00.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/13187",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1821929",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}