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<p>Skin cancer has recently become the fifth most common cancer worldwide, burdening both the economy and global health. Industrialization, genetic modification, and the rapidly changing environment have all contributed to an increase in the incidence of skin cancer. To overcome this challenge, this research suggests employing Fuzzy Self-Guided Structure Convolution Retention Generative Adversarial Network with Greater Cane Rat Algorithm (FSGSCR-GAN-GCRA) approaches to improve the identification accuracy of skin cancer. Initially, dermoscopic images are collected from the ISIC 2017 and ISIC 2018 datasets for analysis. Before this, preprocessing is performed using the Dual Bilateral Least Squares Hybrid Filter (DBLSF), which helps protect edges and remove outliers effectively. This is followed by the Feedback DenseNet201 Network (FDNet201), which enhances feature extraction and accentuates clinically significant regions by jointly using feedback attention strategies with the dense connections of DenseNet201. The Fuzzy Self-Guided Structure Convolution Retention GAN (FSGSCR-GAN) then applies Fuzzy logic and convolutional transformers to the extracted features to retain the structural integrity of lesions, local textures, and global patterns simultaneously. To overcome structural information loss, feature under-representation, and training instability, the GAN enables structure-preserving adversarial learning; the Feedback attention-enhanced DenseNet201 is more effective at lesion-specific feature discrimination; and optimization with the GCRA ensures efficient convergence of hyperparameters. The FSGSCR-GAN-GCRA shows better skin cancer identification than the current methodology. The proposed model has excellent Accuracy (99.71% on ISIC 2017 and 99.81% on ISIC 2018), precision (99.61% on ISIC 2017 and 99.69% on ISIC 2017), F-score (99.64% on ISIC 2017 and 99.69% on ISIC 2017), specificity (99.54% on ISIC 2017 and and 99.60% on ISIC 2017), significantly improving skin cancer identification while minimizing false positives, making it highly reliable for dermoscopic identification.</p>

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Enhanced Dermoscopic Image Analysis for Skin Cancer Identification Using Fuzzy Self-Guided Convolutional Retention GAN with Bio-Inspired Greater Cane Rat Optimization

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • S. Baji,
  • S. Bagal,
  • S. Chaudhari,
  • B. Agarkar

摘要

Skin cancer has recently become the fifth most common cancer worldwide, burdening both the economy and global health. Industrialization, genetic modification, and the rapidly changing environment have all contributed to an increase in the incidence of skin cancer. To overcome this challenge, this research suggests employing Fuzzy Self-Guided Structure Convolution Retention Generative Adversarial Network with Greater Cane Rat Algorithm (FSGSCR-GAN-GCRA) approaches to improve the identification accuracy of skin cancer. Initially, dermoscopic images are collected from the ISIC 2017 and ISIC 2018 datasets for analysis. Before this, preprocessing is performed using the Dual Bilateral Least Squares Hybrid Filter (DBLSF), which helps protect edges and remove outliers effectively. This is followed by the Feedback DenseNet201 Network (FDNet201), which enhances feature extraction and accentuates clinically significant regions by jointly using feedback attention strategies with the dense connections of DenseNet201. The Fuzzy Self-Guided Structure Convolution Retention GAN (FSGSCR-GAN) then applies Fuzzy logic and convolutional transformers to the extracted features to retain the structural integrity of lesions, local textures, and global patterns simultaneously. To overcome structural information loss, feature under-representation, and training instability, the GAN enables structure-preserving adversarial learning; the Feedback attention-enhanced DenseNet201 is more effective at lesion-specific feature discrimination; and optimization with the GCRA ensures efficient convergence of hyperparameters. The FSGSCR-GAN-GCRA shows better skin cancer identification than the current methodology. The proposed model has excellent Accuracy (99.71% on ISIC 2017 and 99.81% on ISIC 2018), precision (99.61% on ISIC 2017 and 99.69% on ISIC 2017), F-score (99.64% on ISIC 2017 and 99.69% on ISIC 2017), specificity (99.54% on ISIC 2017 and and 99.60% on ISIC 2017), significantly improving skin cancer identification while minimizing false positives, making it highly reliable for dermoscopic identification.