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Skin Cancer01:30

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Optimized self-attention based cycle-consistent generative adversarial network adopted melanoma classification from

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This study introduces a novel AI method for accurate melanoma classification from dermoscopic images. The Self-attention based cycle-consistent generative adversarial network optimized with Archerfish Hunting Optimization Algorithm (SACCGAN-AHOA-MC-DI) significantly improves detection accuracy and reduces computational time.

Keywords:
archerfish hunting optimization algorithmdermoscopic imageshexadecimal local adaptive binary patternmelanoma classification

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Area of Science:

  • Dermatology and Artificial Intelligence
  • Medical Image Analysis
  • Computational Pathology

Background:

  • Skin cancer, particularly melanoma, poses a significant health risk, necessitating early and accurate detection.
  • Dermoscopic images are crucial for diagnosing skin lesions, but their analysis can be challenging.
  • Existing methods for melanoma classification often require improvement in accuracy and efficiency.

Purpose of the Study:

  • To propose an advanced AI model for the precise classification of various skin cancers, including melanoma, from dermoscopic images.
  • To enhance the accuracy and reduce the computational time of skin cancer detection systems.
  • To introduce a novel optimization algorithm for improving deep learning-based medical image classification.

Main Methods:

  • Utilized the ISIC 2019 dataset for skin dermoscopic images.
  • Pre-processed images using Adjusted Quick Shift Phase Preserving Dynamic Range Compression (AQSP-DRC) for noise reduction and quality enhancement.
  • Employed Piecewise Fuzzy C-Means Clustering (PF-CMC) for Region of Interest (ROI) segmentation and Hexadecimal Local Adaptive Binary Pattern (HLABP) for radiomic feature extraction.
  • Developed a Self-attention based Cycle-Consistent Generative Adversarial Network (SACCGAN) optimized with the Archerfish Hunting Optimization Algorithm (AHOA) for classification.

Main Results:

  • The proposed SACCGAN-AHOA-MC-DI method achieved higher accuracy compared to existing methods (CNN-BES-MC-DI, CNN-GWOA-MC-DI, DEANN-MC-DI).
  • Demonstrated significant reductions in computational time compared to the benchmarked methods.
  • Successfully classified various skin cancers including Melanocytic nevus, Basal cell carcinoma, Actinic Keratosis, Benign keratosis, Dermatofibroma, Vascular lesion, Squamous cell carcinoma, and melanoma.

Conclusions:

  • The SACCGAN-AHOA-MC-DI model offers a robust and efficient solution for automated skin cancer detection from dermoscopic images.
  • The integration of AHOA optimization significantly enhances the classification performance of the SACCGAN model.
  • This AI-driven approach holds promise for improving early diagnosis rates and patient outcomes in dermatology.