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Skin Cancer01:30

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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.
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Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms.

Areej Alotaibi1, Duaa AlSaeed1

  • 1College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

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|January 11, 2025
PubMed
Summary

Attention mechanisms significantly improved deep learning models for skin cancer detection, increasing accuracy and recall. This advancement aids in earlier diagnosis and better treatment outcomes for skin lesions.

Keywords:
Xceptionattention mechanismcomputer visiondeep learningdermoscopic imagesmedical imagingpre-trained modelsskin cancertransfer learning

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Dermatology

Background:

  • Accurate skin cancer diagnosis is vital for survival but challenging due to similar lesion pigmentation.
  • Deep learning and transfer learning show promise for skin cancer image analysis.
  • Attention mechanisms enhance deep learning accuracy in medical image classification.

Purpose of the Study:

  • To investigate the impact of attention mechanisms (AMs) on the Xception transfer learning model for binary classification of skin lesions.
  • To evaluate the performance of self-attention, hard attention, and soft attention integrated with the Xception model.
  • To compare the enhanced Xception model's performance against the standard Xception model for skin cancer detection.

Main Methods:

  • Four experiments were conducted using the HAM10000 dataset.
  • Three models integrated self-attention, hard attention, and soft attention mechanisms with the Xception architecture.
  • A baseline model used the standard Xception without attention mechanisms for comparison.

Main Results:

  • The standard Xception model achieved 91.05% accuracy.
  • Integrating attention mechanisms improved accuracy: self-attention (94.11%), soft attention (93.29%), and hard attention (92.97%).
  • The proposed models demonstrated superior recall metrics compared to previous studies.

Conclusions:

  • Attention mechanisms enhance the performance of the Xception model for skin lesion classification.
  • These findings suggest potential for earlier diagnosis and improved treatment outcomes in medical imaging.
  • Further research into AMs can advance AI-driven dermatological diagnostics.