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Updated: Mar 29, 2026

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Automated Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model.

Zekiye Kanat1, Merve Kesim Onal2, Harun Bingol3

  • 1Department of Dermatology, Inonu University, Malatya 44280, Türkiye.

Biomedicines
|March 28, 2026
PubMed
Summary

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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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This summary is machine-generated.

This study introduces a hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) for early skin cancer diagnosis. The novel approach achieved 95.1% accuracy, aiding dermatologists in clinical decisions.

Area of Science:

  • Dermatology and Artificial Intelligence
  • Medical Image Analysis

Background:

  • Skin cancer poses a significant health risk due to its potential for metastasis.
  • Early diagnosis of skin cancer is crucial for timely treatment initiation and improved patient outcomes.

Purpose of the Study:

  • To propose a hybrid model for the early diagnosis of skin cancer.
  • To enhance skin cancer detection accuracy using advanced deep learning techniques.

Main Methods:

  • A hybrid model integrating Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) architectures was developed.
  • The model incorporated k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Neural Network Classifiers, Decision Tree (DT), and Logistic Regression (LR) classifiers.
  • Channel and spatial attention mechanisms were employed, and the HAM10000 dataset was utilized with class weighting for balanced training.
Keywords:
CNNViTattentionclassifiersskin cancer

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Main Results:

  • The proposed hybrid model achieved a high accuracy of 95.1% in skin cancer diagnosis.
  • Performance was superior when compared to standalone CNN and ViT architectures.
  • Fine-tuning and attention mechanisms contributed to improved diagnostic accuracy.

Conclusions:

  • The developed hybrid model demonstrates significant potential as a decision support system for dermatologists.
  • Accurate and early skin cancer diagnosis can be facilitated by this advanced AI approach.
  • Further integration of such models can enhance clinical practice in dermatology.