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Peer-reviewedCC BY 4.0PLOS One

Explainable AI framework for skin lesion imaging based cancer detection

  • Sivarama Prasad Tera1
  • Ravikumar Chinthaginjala2
  • Priya Natha3
  • Asadi Srinivasulu4
  • Fadi Al-Turjman5
  • Faruq Mohammad6
6 affiliations
  1. 1Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati, Assam, India
  2. 2School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
  3. 3Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India
  4. 4Cooperative 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
  5. 5AI, and Software Engineering Departments, AI and IoT research center, AI and Informatics Faculty, Near East University, Mersin, Turkey
  6. 6Department of Chemistry, College of Science, King Saud University, Riyadh, Saudi Arabia
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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.

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Cite

BibTeXRIS
BibTeX
@article{tera2026explainable,
  title = {{Explainable AI framework for skin lesion imaging based cancer detection}},
  author = {Sivarama Prasad Tera and Ravikumar Chinthaginjala and Priya Natha and Asadi Srinivasulu and Fadi Al-Turjman and Faruq Mohammad},
  journal = {PLOS One},
  year = {2026},
  month = oct,
  doi = {10.1371/journal.pone.0359224},
  url = {https://doi.org/10.1371/journal.pone.0359224},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Explainable AI framework for skin lesion imaging based cancer detection
AU  - Sivarama Prasad Tera
AU  - Ravikumar Chinthaginjala
AU  - Priya Natha
AU  - Asadi Srinivasulu
AU  - Fadi Al-Turjman
AU  - Faruq Mohammad
PY  - 2026
DA  - 2026/10/06
JO  - PLOS One
DO  - 10.1371/journal.pone.0359224
UR  - https://doi.org/10.1371/journal.pone.0359224
AB  - 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.
SN  - 1932-6203
ER  -