{
  "abstract": "Lung CT slice classification may support image-level decision assistance, but deterministic neural networks do not explicitly indicate when a prediction is unreliable. This study presents a lightweight uncertainty-aware convolutional neural network (CNN) for three-class classification of benign, malignant, and normal CT slices, integrating Monte Carlo (MC) Dropout, validation-selected selective referral, and Grad-CAM visualization. After removing 43 exact duplicate files, 1,054 IQ-OTH/NCCD images were analyzed using an image-level split of 842 training, 106 validation, and 106 test images (seed 42). Because reliable patient identifiers could not be recovered, the study is considered an internal image-level proof-of-concept rather than patient-independent diagnostic validation. With 50-pass MC Dropout, the model correctly classified 105/106 test slices, achieving 99.06% accuracy (exact 95% CI: 94.86–99.98%) and a weighted F1-score of 99.04%. The single misclassified benign slice was predicted as normal and exhibited high predictive uncertainty. Selective referral was based on the scalar uncertainty score $${\\text{U}} = {\\text{max}}\\left( {1 - {\\text{Confidence}},\\,{\\text{H}}_{{{\\text{norm}}}} ,\\,{\\text{MI}}_{{{\\text{norm}}}} ,\\,{\\text{VR}}} \\right)$$ , with a threshold selected as the 80th percentile of validation-set U values $$\\left( {\\tau_{U} = 0.174166} \\right)$$ , without using test labels or test performance. This operating point accepted 88/106 slices (83.02% coverage) and referred 18/106 (16.98%), with an observed accepted accuracy of 100% (95% CI: 95.89–100%) and an observed selective risk of 0% (95% CI: 0–4.11%). Calibration analysis yielded a 10-bin equal-width expected calibration error of 0.0295, a multiclass Brier score of 0.0137, and a negative log-likelihood of 0.0385. Deep Ensemble and Ensemble MC Dropout each correctly classified all 106 test slices, although these results remain subject to the same image-level data limitation. Simulated low-contrast and Gaussian-noise perturbations reduced accuracy and increased referral, indicating responsiveness of the uncertainty score to distributional degradation. A separately retrained binary lightweight CNN variant on the Chest CT-Scan Images Dataset correctly classified all 286 Cancer-versus-Normal test slices (100%; 95% CI: 98.72–100%), but its validation-selected referral rule accepted only 134/286 slices (46.85%) and referred all 25 Normal test slices. Therefore, the second-dataset experiment should be interpreted as a dataset-specific image-level benchmark rather than direct external validation of the primary three-class model. These findings support further patient-wise, multi-center evaluation of uncertainty-aware CT-slice classification and selective referral.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Asmaa M. Khalid"
    },
    {
      "name": "Shimaa M. Abdel-Moniem"
    },
    {
      "name": "Nabil A. Lashin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f6scnj1mjxwcw.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-73582-9",
  "fulltext_url": null,
  "id": "f6scnj1mjxwcw",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-73582-9",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": "https://www.nature.com/articles/s41598-026-73582-9.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/f6scnj1mjxwcw.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:20:35Z",
      "native_id": "10.1038/s41598-026-73582-9",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-73582-9"
    }
  ],
  "subjects": [],
  "title": "Uncertainty-aware lung CT slice classification using a lightweight CNN with selective referral",
  "title_text": "Uncertainty-aware lung CT slice classification using a lightweight CNN with selective referral",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T00:20:51Z",
  "url": "https://research.draconitic.com/paper/f6scnj1mjxwcw",
  "venue": "Scientific Reports",
  "versions": []
}