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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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Active Learning with Particle Swarm Optimization for Enhanced Skin Cancer Classification Utilizing Deep CNN Models.

Sayantani Mandal1, Subhayu Ghosh2, Nanda Dulal Jana2

  • 1Department of Mathematics, National Institute of Technology Durgapur, West Bengal, India.

Journal of Imaging Informatics in Medicine
|November 18, 2024
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Summary

This study introduces an efficient skin cancer classification framework using active learning (AL) and particle swarm optimization (PSO). The AL-PSO method significantly improves accuracy while using less labeled data for AI-driven skin cancer detection.

Keywords:
Active learningConvolutional neural networksMedical imagingParticle swarm optimizationSkin cancer classification

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Area of Science:

  • Dermatology and Artificial Intelligence
  • Computational Biology and Bioinformatics

Background:

  • Skin cancer, including melanoma and non-melanoma types, poses a significant global health challenge, necessitating early detection for improved patient outcomes.
  • Traditional deep learning models for skin cancer classification often require extensive annotated datasets and substantial computational power, limiting their widespread clinical application.

Purpose of the Study:

  • To develop an efficient skin cancer classification framework by integrating active learning (AL) with particle swarm optimization (PSO).
  • To address the limitations of traditional deep learning models by reducing the need for large labeled datasets and computational resources in skin cancer detection.

Main Methods:

  • An active learning (AL) framework was employed to intelligently select the most informative unlabeled skin lesion images for expert annotation, thereby minimizing labeling costs.
  • Particle swarm optimization (PSO) was integrated into the AL process to enhance the selection of relevant data points, optimizing classifier performance.
  • Multiple Convolutional Neural Network (CNN) models were trained using the proposed AL-PSO approach on the HAM10000 skin lesion dataset.

Main Results:

  • The AL-PSO approach demonstrated a significant improvement in classification accuracy for skin cancer detection.
  • Using the Least Confidence strategy within the AL-PSO framework, approximately 89.4% accuracy was achieved while utilizing only 40% of the labeled training data.
  • This approach offers substantial gains in both accuracy and efficiency compared to traditional methods.

Conclusions:

  • The integration of active learning (AL) and particle swarm optimization (PSO) presents a highly effective strategy for enhancing AI-based skin cancer classification.
  • This efficient framework has the potential to accelerate the adoption of artificial intelligence in clinical settings for early and accurate skin cancer detection.
  • The study provides a publicly available codebase to facilitate further research and development in this area.