{
  "abstract": "We present a comparative analysis of six models belonging to Convolutional Neural Network (CNN), Transformer, and Mamba families for automated eye disease classification from fundus images under identical experimental conditions. To the best of our knowledge, our work is the first to explore Mamba-based models for automated multi-class eye disease classification from color fundus images. Our analysis suggests that, when evaluated fairly, modern CNN, Transformer, and Mamba-based models provide similar performance for eye disease classification on the Eye Disease Image Dataset (EDID). Along with analyzing individual model performance, we further evaluate performance using a logit-level ensemble of the top three models. The ensemble results imply that these model families capture partially complementary visual features, resulting in a slight performance gain when combined. In both cases, class-wise performance examination indicates that diseases with distinctive structural characteristics are recognized more consistently, while categories with subtle or overlapping features remain challenging. Our implementation, including the training and evaluation pipelines as well as the data splits, is publicly available at https://github.com/ahanaf019/EyeDisease2026.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science and Engineering, Faculty of Engineering, University of Rajshahi, Rajshahi, Bangladesh."
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Md Ahanaf Arif Khan",
      "orcid": "0009-0005-6098-8318"
    },
    {
      "affs": [
        0
      ],
      "name": "Sangeeta Biswas",
      "orcid": "0000-0001-6322-8836"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7j19km6sn1sdd.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0360171",
  "fulltext_url": "https://europepmc.org/articles/PMC13704858",
  "id": "7j19km6sn1sdd",
  "issns": [
    "1932-6203"
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  "landing_url": "https://doi.org/10.1371/journal.pone.0360171",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "openalex",
      "2741|Retinal Imaging and Analysis"
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      "plos",
      "/Biology and life sciences/Anatomy/Head/Eyes"
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      "/Biology and life sciences/Anatomy/Ocular system/Eyes"
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      "/Biology and life sciences/Anatomy/Ocular system/Ocular anatomy/Optic disc"
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      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Perception/Sensory perception/Vision"
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      "plos",
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      "/Medicine and health sciences/Medical conditions/Eye diseases"
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      "/Medicine and health sciences/Medical conditions/Eye diseases/Retinal disorders"
    ],
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      "/Medicine and health sciences/Medical conditions/Eye diseases/Retinal disorders/Retinal detachment"
    ],
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      "plos",
      "/Medicine and health sciences/Ophthalmology/Eye diseases"
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      "plos",
      "/Medicine and health sciences/Ophthalmology/Eye diseases/Retinal disorders"
    ],
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      "/Medicine and health sciences/Ophthalmology/Eye diseases/Retinal disorders/Retinal detachment"
    ],
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      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Perception/Sensory perception/Vision"
    ]
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  "pdf_url": "https://europepmc.org/articles/PMC13704858?pdf=render",
  "pmcid": "PMC13704858",
  "pmid": "42932327",
  "published": "2026-10-26",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/7j19km6sn1sdd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.1371/journal.pone.0360171",
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      "url": "https://doi.org/10.1371/journal.pone.0360171"
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    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "MED:42932327",
      "source": "europepmc",
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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    {
      "field": "medicine",
      "origin": "plos:anatomy",
      "subfield": "physiology"
    }
  ],
  "title": "Comparative analysis of CNN, Transformer, and Mamba-based models for automated eye disease classification from fundus images",
  "title_text": "Comparative analysis of CNN, Transformer, and Mamba-based models for automated eye disease classification from fundus images",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/7j19km6sn1sdd",
  "venue": "PLOS One",
  "versions": []
}