{
  "abstract": "This study presents EffAtt-LungNet, an attention-guided deep learning framework for three-class lung histopathology classification. The model combines an EfficientNetB3 backbone with the Convolutional Block Attention Module (CBAM) to refine features and improve discrimination among benign lung tissue, lung adenocarcinoma, and lung squamous cell carcinoma. Experiments used the publicly released LC25000 lung subset, which contains 15,000 images across three classes and was itself produced by augmenting a smaller collection of underlying histopathology images. We generated 10,500 additional fixed variants, creating a balanced working corpus of 25,500 images with training, validation, and test partitions of 15,300, 2,550, and 7,650 images. EffAtt-LungNet reached 99.86% accuracy, 99.86% precision, 99.86% recall, 99.86% F1 score, and 0.9997 AUC on the image-level test partition. Performance was stable across five random seeds (mean accuracy 99.86±0.02%), and paired errors differed from those of the EfficientNetB3 backbone (McNemar’s test, χ2=47.27, p < 0.001). Because pre-augmentation source identifiers for the released LC25000 images were unavailable, independence at the earliest source-image level could not be confirmed. The results therefore represent internal tile-level benchmark performance and require validation on independent, non-augmented, multi-centre datasets before any translational or clinical claim can be made.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Software Engineering, Faculty of Computing, The Islamia University of Bahawalpur, Bahawalpur, Pakistan",
    "Department of Computer Science, National University of Modern Languages (NUML), Islamabad, Pakistan",
    "School of Informatics and Creative Arts, Dundalk Institute of Technology, Dundalk, Ireland"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
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        0
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      "name": "Muhammad Ahsan Jamil"
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    {
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      "name": "Malik Daler Ali Awan"
    },
    {
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        2
      ],
      "name": "Fatima Bukhari"
    },
    {
      "name": "Malik Muhammad Saad Missen"
    },
    {
      "name": "Nadeem Iqbal Kajla"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0azpkbcv8t3ah.bib",
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  "doi": "10.1371/journal.pone.0359302",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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    ],
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      "/Medicine and health sciences/Diagnostic medicine/Diagnostic radiology/Pulmonary imaging"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Carcinoma/Adenocarcinoma"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Carcinoma/Adenocarcinoma/Adenocarcinoma of the lung"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Carcinoma/Squamous cell carcinoma"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Carcinoma/Squamous cell carcinoma/Squamous cell lung carcinoma"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors/Adenocarcinoma of the lung"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors/Non-small cell lung cancer"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Lung and intrathoracic tumors/Squamous cell lung carcinoma"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pathology and laboratory medicine/Anatomical pathology/Histopathology"
    ],
    [
      "plos",
      "/Medicine and health sciences/Radiology and imaging/Diagnostic radiology/Pulmonary imaging"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Diagnostic radiology/Pulmonary imaging"
    ]
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359302&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/0azpkbcv8t3ah.ris",
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    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0359302",
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      "url": "https://doi.org/10.1371/journal.pone.0359302"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:cancers and neoplasms",
      "subfield": "oncology"
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  "title": "EffAtt-LungNet: An attention-guided EfficientNet framework for three-class lung histopathology classification",
  "title_text": "EffAtt-LungNet: An attention-guided EfficientNet framework for three-class lung histopathology classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/0azpkbcv8t3ah",
  "venue": "PLOS One",
  "versions": []
}