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Updated: Jan 19, 2026

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Gabor wavelet-based deep learning for skin lesion classification.

Sertan Serte1, Hasan Demirel2

  • 1Electrical and Electronic Engineering, Near East University, Nicosia, North Cyprus via Mersin 10, Turkey. Electronic address: http://www.neu.edu.tr.

Computers in Biology and Medicine
|September 10, 2019
PubMed
Summary

This study introduces a novel deep learning method using Gabor wavelets for accurate skin cancer detection. The approach enhances early diagnosis of malignant melanoma and seborrheic keratosis, outperforming existing techniques.

Keywords:
Convolutional neural networksGabor waveletsModel fusion

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Area of Science:

  • Dermatology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computational Pathology

Background:

  • Increasing global incidence of skin cancer necessitates improved diagnostic tools.
  • Accurate and early detection of malignant melanoma and seborrheic keratosis is critical for effective treatment.
  • Current diagnostic methods may face challenges in speed and accuracy for common skin lesions.

Purpose of the Study:

  • To propose a novel deep convolutional neural network (CNN) approach for the detection of malignant melanoma and seborrheic keratosis.
  • To leverage Gabor wavelet transforms for enhanced image decomposition and feature extraction in skin lesion analysis.
  • To develop a robust classification system through decision fusion of multiple CNN outputs.

Main Methods:

  • A novel Gabor wavelet-based deep convolutional neural network (CNN) was developed.
  • Input images were decomposed into seven directional sub-bands using Gabor wavelets.
  • Seven sub-band images and the original image were processed by eight parallel CNNs.
  • A sum rule-based decision fusion strategy was employed for final classification.

Main Results:

  • The proposed Gabor wavelet-based CNN method demonstrated high performance in detecting malignant melanoma and seborrheic keratosis.
  • Directional decomposition via Gabor wavelets facilitated isolated decisions from sub-bands, improving overall accuracy.
  • The fusion of multiple CNN predictions led to enhanced classification performance.
  • The method outperformed existing alternative approaches for skin cancer detection in the literature.

Conclusions:

  • The Gabor wavelet-based deep CNN offers a promising and effective approach for accurate skin lesion classification.
  • This method provides a significant advancement in the early detection of common skin cancers.
  • The proposed technique shows potential for improving diagnostic accuracy and patient outcomes in dermatology.