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Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural

Theyazn H H Aldhyani1, Amit Verma2, Mosleh Hmoud Al-Adhaileh3

  • 1Applied College in Abqaiq, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.

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Summary
This summary is machine-generated.

A new lightweight AI model accurately classifies skin lesions, achieving 97.85% accuracy on the HAM10000 dataset. This advancement offers efficient and precise detection of various skin diseases.

Keywords:
artificial intelligencebiomedical imagedeep learningskin diseases

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Area of Science:

  • Dermatology and Artificial Intelligence
  • Medical Image Analysis

Background:

  • Skin diseases are a growing global concern, exacerbated by pollution and other factors.
  • Accurate classification of diverse skin lesions is a significant challenge in dermatology.
  • Existing models for skin lesion classification are often computationally intensive.

Purpose of the Study:

  • To develop a lightweight and highly accurate model for skin lesion classification.
  • To address the need for efficient diagnostic tools in dermatology.
  • To improve the accuracy of identifying various types of skin lesions.

Main Methods:

  • Development of a novel deep learning model utilizing dynamic-sized kernels.
  • Incorporation of both ReLU and leakyReLU activation functions.
  • Training and evaluation on the comprehensive HAM10000 skin lesion dataset.

Main Results:

  • The proposed model achieved an outstanding overall accuracy of 97.85%.
  • The model successfully classified all classes within the HAM10000 dataset.
  • Demonstrated superior performance compared to several state-of-the-art heavy models.

Conclusions:

  • The developed lightweight model provides a highly accurate solution for skin lesion classification.
  • This approach offers a more efficient alternative to existing, heavier models.
  • The findings suggest significant potential for AI in improving dermatological diagnostics.