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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Classification of Epithelial Tissues: Overview01:22

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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
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Related Experiment Video

Updated: Jan 16, 2026

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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MultiExCam: A multi approach and explainable artificial intelligence architecture for skin lesion classification.

Tommaso Ruga1, Luciano Caroprese2, Eugenio Vocaturo1

  • 1DIMES - University of Calabria, Via P. Bucci, 44z Cube, Rende (CS), 87036, Italy; CNR-NANOTEC, Via P. Bucci, 33B Cube, Rende (CS), 87036, Italy.

Computer Methods and Programs in Biomedicine
|September 29, 2025
PubMed
Summary

This study introduces MultiExCam, an AI tool that combines machine and deep learning for early skin cancer detection. It achieves high accuracy and provides explanations for its predictions, aiding clinical decisions.

Keywords:
Ensemble learningExplainable AIMelanomaSkin lesionTransfer learning

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Area of Science:

  • Artificial Intelligence in Dermatology
  • Medical Image Analysis
  • Computational Pathology

Background:

  • Cutaneous melanoma is a lethal skin cancer, but early diagnosis significantly improves survival rates.
  • Existing AI solutions for skin lesion diagnosis often use machine learning and deep learning in isolation.
  • There is a need for integrated AI approaches that combine multiple data sources and provide interpretable results.

Purpose of the Study:

  • To introduce MultiExCam, a novel, explainable, multi-approach AI architecture for skin cancer detection.
  • To integrate heterogeneous data sources including dermatoscopic images and extracted features.
  • To develop an AI system that combines machine learning and deep learning for enhanced diagnostic performance and interpretability.

Main Methods:

  • MultiExCam utilizes a hybrid architecture integrating deep learning (CNN) for feature extraction and initial classification with machine learning models.
  • It combines deep learning features with hand-crafted statistical features for training ensemble models.
  • An advanced ensemble model with gating and attention mechanisms provides the final classification, enhanced by GradCAM and SHAP for explainability.

Main Results:

  • MultiExCam achieved high performance across diverse datasets with AUC scores up to 98% and F1-scores up to 94%.
  • The hybrid ensemble approach outperformed baseline deep learning models by 1-3%.
  • Explainability analysis identified clinically relevant patterns linked to diagnostic criteria like asymmetry and irregular borders.

Conclusions:

  • MultiExCam sets a new standard for AI-assisted dermatological diagnosis by integrating deep learning, machine learning, and explainability.
  • The AI's ability to provide accurate, interpretable predictions addresses key requirements for clinical adoption.
  • This architecture offers a strong foundation for AI-driven clinical decision support systems in melanoma detection.