{
  "abstract": "Class imbalance, poor confidence calibration, and limited generalization under varying imaging conditions are major challenges in automated dermoscopic skin lesion analysis. We propose a hybrid classifier, MaxCAT-Net (MaxViT + CBAM + Attention Transformer), consisting of a pretrained MaxViT-Tiny backbone combined with CBAM and a lightweight two-layer Transformer encoder. We also evaluate four encoder-decoder networks for lesion segmentation as a separate task using ISIC 2018 Task 1. After removing exact and near-duplicate images, the remaining 10,012 images were divided into lesion-wise training, validation, and test sets using a 72/14/14 split, ensuring that images from the same lesion did not appear across multiple subsets. On the test set (n = 1,405), MaxCAT-Net outperformed the ResNet152-CBAM, MobileNetV3-CBAM, GhostNet-hybrid, and standalone MaxViT-Tiny-CBAM baselines, achieving an accuracy of 0.869 (95% CI: 0.847-0.890), balanced accuracy of 0.742, macro-F1 of 0.751, and macro-AUC of 0.944. However, ablation-related macro-F1 improvements (0.017-0.022) were not statistically conclusive. Temperature scaling reduced the expected calibration error (ECE) from 0.099 to 0.038. On the PAD-UFES-20 dataset, accuracy decreased to 0.341 and melanoma recall to 0.096, while melanoma recall on HAM10000 was only 0.587. For segmentation, a Swin-T-encoder U-Net achieved a Dice score of 0.903 on 520 test images, whereas a FasterNet-T0 U-Net, with approximately one-tenth the number of parameters, achieved a Dice score of 0.901 (Holm-adjusted p < 0.002). A standard U-Net achieved a significantly lower Dice score of 0.888. All results are based on a single random seed and should therefore be interpreted as preliminary trends rather than definitive evidence of improvement.",
  "abstract_shown": true,
  "affiliations": [
    "International Islamic University Chittagong"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. A. Prince"
    },
    {
      "name": "I. F. Shorna"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2b0fweebcah11.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.01.763389",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.01.763389v1.full",
  "id": "2b0fweebcah11",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.01.763389v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioengineering"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.01.763389v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2b0fweebcah11.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:05Z",
      "native_id": "10.64898/2026.11.01.763389",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.01.763389v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioengineering",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "MaxCAT-Net: Hybrid Transformer-Based Skin Lesion Classification and Lightweight Segmentation with Calibration and Cross-Dataset Validation",
  "title_text": "MaxCAT-Net: Hybrid Transformer-Based Skin Lesion Classification and Lightweight Segmentation with Calibration and Cross-Dataset Validation",
  "type": "preprint",
  "updated_at": "2026-11-04T00:21:18Z",
  "url": "https://research.draconitic.com/paper/2b0fweebcah11",
  "venue": "bioRxiv",
  "versions": []
}