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<p>The last few years have witnessed rapid increase in skin cancer caused mortality rate. Despite innovations and growth in vision-computing and artificial intelligence technologies, the complex shapes, sizes, textural patterns and ambiguous edges limits the reliability of existing approaches. Nevertheless, unlike traditional approaches the deep learning methods have performed superior; yet, the demands for the superior skin-lesion segmentation, ROI-specific feature extraction and learning can’t be ruled out. Moreover, it requires addressing class-imbalance problems as well to avoid skewed learning and prediction. Considering it as motivation, in this paper a novel and robust semantic segmentation assisted deep ensemble feature learning environment for skin-cancer detection and classification (SDENet) is proposed. The proposed SDENet model is targeted to perform multi-class skin-cancer classification. To achieve it, the SDENet at first performs standard pre-processing followed by synthetic minority over-sampling (SMOTE) to alleviate class-imbalance problem. Subsequently, it performs firefly heuristic algorithm based Fuzzy C-means clustering to segment skin-lesions (say, ROI), which is followed by ROI-specific deep spatio-textural ensemble feature extraction and fusion (DeS-TEFF). Specifically, SDENet makes use of the AlexNet deep network, DenseNet121 and Gray level co-occurrence matrix (GLCM) feature extraction methods. Here, AlexNet serves high-dimensional information rich features, while DenseNet121 yields layer-wise learning and feature reuse driven feature-set. Performing horizontal concatenation over the AlexNet, DenseNet121 and GLCM features, the principal component analysis (PCA) feature selection was performed, which helped to avoid local minima and convergence. The selected features were normalized by means of the z-score normalization so as to avoid over-fitting problems. Finally, the normalized features were trained and classified by using Heterogenous Ensemble Classifier, embodying SVM, DT, Random Forest, Extra Tree Classifier and XGBoost classifiers. The maximum voting ensemble-based classification over HAM10000 dataset exhibited the average accuracy of 98.97%, precision 99.38%, recall 98.94% and F-Measure 0.99, confirming its superiority over other existing approaches for real-time skin cancer diagnosis purposes.</p>

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Semantic segmentation assisted deep ensemble feature learning model for skin-cancer detection and classification: SDENet

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月5日

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  • Ch. Srilakshmi,
  • N. Ramakrishnaiah,
  • E. Laxmi Lydia

摘要

The last few years have witnessed rapid increase in skin cancer caused mortality rate. Despite innovations and growth in vision-computing and artificial intelligence technologies, the complex shapes, sizes, textural patterns and ambiguous edges limits the reliability of existing approaches. Nevertheless, unlike traditional approaches the deep learning methods have performed superior; yet, the demands for the superior skin-lesion segmentation, ROI-specific feature extraction and learning can’t be ruled out. Moreover, it requires addressing class-imbalance problems as well to avoid skewed learning and prediction. Considering it as motivation, in this paper a novel and robust semantic segmentation assisted deep ensemble feature learning environment for skin-cancer detection and classification (SDENet) is proposed. The proposed SDENet model is targeted to perform multi-class skin-cancer classification. To achieve it, the SDENet at first performs standard pre-processing followed by synthetic minority over-sampling (SMOTE) to alleviate class-imbalance problem. Subsequently, it performs firefly heuristic algorithm based Fuzzy C-means clustering to segment skin-lesions (say, ROI), which is followed by ROI-specific deep spatio-textural ensemble feature extraction and fusion (DeS-TEFF). Specifically, SDENet makes use of the AlexNet deep network, DenseNet121 and Gray level co-occurrence matrix (GLCM) feature extraction methods. Here, AlexNet serves high-dimensional information rich features, while DenseNet121 yields layer-wise learning and feature reuse driven feature-set. Performing horizontal concatenation over the AlexNet, DenseNet121 and GLCM features, the principal component analysis (PCA) feature selection was performed, which helped to avoid local minima and convergence. The selected features were normalized by means of the z-score normalization so as to avoid over-fitting problems. Finally, the normalized features were trained and classified by using Heterogenous Ensemble Classifier, embodying SVM, DT, Random Forest, Extra Tree Classifier and XGBoost classifiers. The maximum voting ensemble-based classification over HAM10000 dataset exhibited the average accuracy of 98.97%, precision 99.38%, recall 98.94% and F-Measure 0.99, confirming its superiority over other existing approaches for real-time skin cancer diagnosis purposes.