{
  "abstract": "The classification of skin cancer remains a challenging task because of the subtle visual patterns of benign and malignant lesions. In this work we propose an interpretable ensemble learning framework for dermoscopic skin lesion classification by combining Random Forest, XGBoost and LightGBM using Max-Voting. The proposed system demonstrates good predictive performance with clinical interpretability, achieving 95.94% accuracy on the HAM10000 dataset. To increase transparency we use explainable AI techniques to visualise the image regions and features that are most important for the model’s decisions. We also perform feature selection based on Genetic Algorithm to select the most discriminative descriptors and to reduce the redundancy in the hybrid feature space. The combination of deep features, handcrafted features, ensemble learning and explainability leads to a strong framework for accurate prediction and meaningful interpretation. The experimental results show that the proposed approach outperforms the individual classifiers and provides more explainable clinically relevant explanations that may help to improve the confidence and trust of dermatologists. In summary, the study demonstrates that the combination of ensemble learning and explainable AI can provide an effective and practical direction towards trustworthy skin cancer decision support.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati, Assam, India",
    "School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India",
    "Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India",
    "Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), Global Centre for Environmental Remediation/College of Engineering Science & Environment, The University of Newcastle, Callaghan, New South Wales, Australia",
    "AI, and Software Engineering Departments, AI and IoT research center, AI and Informatics Faculty, Near East University, Mersin, Turkey",
    "Department of Chemistry, College of Science, King Saud University, Riyadh, Saudi Arabia"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sivarama Prasad Tera"
    },
    {
      "affs": [
        1
      ],
      "name": "Ravikumar Chinthaginjala"
    },
    {
      "affs": [
        2
      ],
      "name": "Priya Natha"
    },
    {
      "affs": [
        3
      ],
      "name": "Asadi Srinivasulu"
    },
    {
      "affs": [
        4
      ],
      "name": "Fadi Al-Turjman"
    },
    {
      "affs": [
        5
      ],
      "name": "Faruq Mohammad"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/96rmnr9n1ae43.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0359224",
  "fulltext_url": null,
  "id": "96rmnr9n1ae43",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0359224",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2730|Cutaneous Melanoma Detection and Management"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Machine learning algorithms"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Lesions"
    ],
    [
      "plos",
      "/Medicine and health sciences/Dermatology/Skin neoplasms/Malignant skin neoplasms/Cutaneous melanoma"
    ],
    [
      "plos",
      "/Medicine and health sciences/Dermatology/Skin neoplasms/Skin tumors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Cancer detection and diagnosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancer detection and diagnosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Melanoma/Cutaneous melanoma"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Skin neoplasms/Malignant skin neoplasms/Cutaneous melanoma"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Skin neoplasms/Skin tumors"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Machine learning algorithms"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Machine learning algorithms"
    ]
  ],
  "openalex": "W7220729619",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0359224&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/96rmnr9n1ae43.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.1371/journal.pone.0359224",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0359224"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "plos:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Explainable AI framework for skin lesion imaging based cancer detection",
  "title_text": "Explainable AI framework for skin lesion imaging based cancer detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/96rmnr9n1ae43",
  "venue": "PLOS One",
  "versions": []
}