{
  "abstract": "This study evaluates the integration of attention mechanisms (Efficient Channel Attention [ECA] and Coordinate Attention [CoordAtt]) into MobileNetV3-Small for preliminary conjunctival anemia screening. Using a single public dataset of 710 pediatric images from Ghana (CP-AnemiC dataset), the model was evaluated retrospectively via stratified 10-fold cross-validation (80%) and tested on a hold-out test set (20%). CoordAtt achieved the highest performance (Accuracy 78%, Sensitivity 84%, Specificity 70%, AUC 0.85), outperforming the Baseline (Accuracy 75%, Sensitivity 80%, Specificity 67%, AUC 0.80) and ECA (Accuracy 76%, Sensitivity 83%, Specificity 66%, AUC 0.81). A 95% Bootstrap BCa confidence interval test confirmed that the CoordAtt improvement over the Baseline is statistically significant for Accuracy, F1-Macro, Sensitivity, and AUC. Grad-CAM visualizations further showed more focused conjunctival localization. This improvement came with only a minimal increase in theoretical computational footprint (1.06M parameters, 0.0584 GFLOPs), keeping a lightweight profile similar to the Baseline (1.03M parameters, 0.0581 GFLOPs). These results show that CoordAtt improves classification performance without sacrificing computational efficiency, suggesting potential suitability for resource-constrained devices prior to clinical validation.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "Arnanda Asshaffa Mahendra"
    },
    {
      "name": "Aris Marjuni"
    },
    {
      "name": "Affandy"
    },
    {
      "name": "Muhammad Naufal"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6n7f79dawnhbd.bib",
  "cited_by": 0,
  "doi": "10.63158/journalisi.v8i5.1796",
  "fulltext_url": "https://journal-isi.org/index.php/isi/article/view/1796",
  "id": "6n7f79dawnhbd",
  "issns": [
    "2656-5935",
    "2656-4882"
  ],
  "landing_url": "https://doi.org/10.63158/journalisi.v8i5.1796",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QA1-939"
    ],
    [
      "lcc",
      "QA75.5-76.95"
    ],
    [
      "openalex",
      "2741|Retinal Imaging and Analysis"
    ]
  ],
  "openalex": "W7223357194",
  "pdf_url": "https://journal-isi.org/index.php/isi/article/download/1796/1091",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-24",
  "publisher": "Asosiasi Doktor Sistem Informasi Indonesia",
  "ris": "https://research.draconitic.com/paper/6n7f79dawnhbd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:44Z",
      "native_id": "3f476571aab04840919ac8d3e6126cda",
      "source": "doaj",
      "url": "https://doi.org/10.63158/journalisi.v8i5.1796"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Retinal Imaging and Analysis",
      "subfield": "imaging"
    }
  ],
  "title": "Enhancing Conjunctival Image-Based Anemia Classification Using Coordinate Attention on MobileNetV3-Small",
  "title_text": "Enhancing Conjunctival Image-Based Anemia Classification Using Coordinate Attention on MobileNetV3-Small",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:44Z",
  "url": "https://research.draconitic.com/paper/6n7f79dawnhbd",
  "venue": "Journal of Information Systems and Informatics",
  "versions": []
}