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Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine.

Farhat Afza1, Muhammad Sharif1, Muhammad Attique Khan2

  • 1Department of Computer Science, Wah Campus, COMSATS University Islamabad, Wah Cantt 47040, Pakistan.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced deep learning method for classifying skin lesions, improving accuracy and efficiency in detecting skin cancer from dermoscopy images. The novel approach enhances diagnostic capabilities for medical applications.

Keywords:
ELMcontrast enhancementdeep learningevolutionary algorithmsfusionskin cancer

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Manual skin lesion detection is challenging and time-consuming.
  • Deep learning and IoT advancements offer potential improvements in medical diagnostics.
  • Accurate and efficient skin cancer classification is crucial for patient outcomes.

Purpose of the Study:

  • To propose a novel multiclass skin lesion classification method.
  • To enhance accuracy and computational efficiency in skin cancer detection.
  • To leverage deep learning feature fusion and extreme learning machines for improved classification.

Main Methods:

  • Image acquisition and contrast enhancement.
  • Deep learning feature extraction via transfer learning.
  • Hybrid whale optimization and entropy-mutual information (EMI) for feature selection.
  • Modified canonical correlation for feature fusion.
  • Extreme learning machine for final classification.

Main Results:

  • Achieved high accuracy: 93.40% on HAM10000 and 94.36% on ISIC2018 datasets.
  • Demonstrated improved accuracy compared to state-of-the-art (SOTA) techniques.
  • Showcased significant computational efficiency due to optimized feature selection.

Conclusions:

  • The proposed deep learning method offers a highly accurate and efficient solution for multiclass skin lesion classification.
  • The hybrid feature selection and fusion strategy effectively enhances diagnostic performance.
  • This approach holds promise for improving automated skin cancer detection systems.