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A hierarchical three-step superpixels and deep learning framework for skin lesion classification.

Farhat Afza1, Muhammad Sharif1, Mamta Mittal2

  • 1Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.

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Summary

This study introduces a new deep learning framework for skin cancer classification. The method enhances dermoscopy images and uses superpixel segmentation for improved diagnostic accuracy.

Keywords:
Deep learningFeatures optimizationImage fusionLesion segmentationSkin cancer

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Area of Science:

  • Dermatology and Computational Pathology
  • Artificial Intelligence in Medical Imaging

Background:

  • Skin cancer, particularly malignant melanoma, poses a significant global health risk with high mortality rates.
  • Existing computerized methods for skin lesion diagnosis often lack sufficient accuracy.
  • Accurate and early diagnosis of skin cancer is crucial for effective treatment and improved patient outcomes.

Purpose of the Study:

  • To develop and evaluate a novel hierarchical framework for enhanced skin cancer classification.
  • To improve the diagnostic accuracy of automated skin lesion analysis using deep learning and image processing techniques.

Main Methods:

  • A hierarchical framework combining contrast enhancement of dermoscopy images, three-step superpixel lesion segmentation, and deep learning (ResNet-50) was proposed.
  • Feature extraction using ResNet-50 was optimized with an improved grasshopper optimization algorithm.
  • Classification was performed using a Naïve Bayes classifier on the optimized features.

Main Results:

  • The proposed method achieved high classification accuracies on three diverse datasets (Ph2, ISBI2016, HAM1000): 95.40%, 91.1%, and 85.50%, respectively.
  • The framework demonstrated effectiveness across datasets with varying numbers of skin cancer classes (3, 2, and 7 classes).

Conclusions:

  • The developed hierarchical framework significantly improves the accuracy of skin cancer classification from dermoscopy images.
  • This approach offers a promising tool for aiding dermatologists in the accurate diagnosis of skin lesions.