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An Optimal Deep Hybrid Framework with Selective Kernel U-Net for Skin Lesion Detection and Classification.

Guzal Gulmirzaeva1,2, Robert Hudec1, Baxtiyorjon Akbaraliev3

  • 1Department of Multimedia and Information-Communication Technology, University of Žilina, 010 01 Žilina, Slovakia.

Bioengineering (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces an optimal deep hybrid framework for detecting and classifying skin cancer from dermoscopic images. The advanced system achieves high accuracy, aiding in early skin cancer diagnosis and reducing mortality rates.

Area of Science:

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

Background:

  • Early skin cancer detection, especially malignant melanoma, is crucial for reducing mortality.
  • Automated analysis of dermoscopic images offers potential to enhance clinical diagnosis over manual inspection.
  • Challenges in automated analysis include image noise, low contrast, lesion variability, and redundant feature representation.

Purpose of the Study:

  • To develop an optimal deep hybrid framework for robust and efficient skin lesion detection and classification.
  • To integrate advanced preprocessing, precise segmentation, optimal feature selection, and accurate classification for improved diagnostic performance.
  • To address limitations of existing methods by proposing a novel framework for skin cancer analysis.
Keywords:
Fossa Optimization AlgorithmSK-UNet segmentationdeep learningdermoscopic imagesfeature selectionskin cancer detection

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Main Methods:

  • Image preprocessing using Contrast Limited Adaptive Histogram Equalization (CLAHE) and Wiener filtering for contrast enhancement and noise reduction.
  • Lesion segmentation via Selective Kernel U-Net (SK-UNet) to capture multi-scale spatial information.
  • Feature extraction and optimization using the Fossa Optimization Algorithm (FOA) followed by classification with a hybrid 1D-CNN-GRU model.

Main Results:

  • The proposed framework achieved high classification accuracies: 97.6% on the ISIC dataset and 95.6% on the DermMNIST dataset.
  • The system demonstrated superior performance compared to several existing methods in skin lesion classification.
  • The optimized feature selection effectively reduced redundancy and improved classification precision.

Conclusions:

  • The developed deep hybrid framework provides a reliable, accurate, and scalable solution for skin cancer diagnosis.
  • The system shows significant potential in assisting clinical decision-making and facilitating early detection of skin cancer.
  • The integration of advanced techniques in preprocessing, segmentation, feature selection, and classification leads to improved diagnostic outcomes.