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<p>Melanoma presents considerable risks as it is an extremely aggressive type of skin cancer that rapidly metastasize if not addressed promptly. Early detection of melanoma significantly improves treatment outcomes and reduces mortality, aligning with SDG 3 (Good Health and Well-being). Dermatologists face challenges in accurately diagnosing skin lesions and recognizing skin cancer early due to human error and variation in experience. This study develops hybrid Artificial Neural Network (ANN) methodologies integrating CNN-based feature selection algorithms for dermoscopic image analysis, contributing to health system sustainability by enhancing diagnostic accuracy through AI. This approach, tested on the ISIC-2019 dataset, employs advanced filtering and segmentation along with fused multi-model CNN features and Ant Colony Optimization (ACO) for feature selection. The fused features generated from MobileNet, ResNet34, and GoogLeNet are subsequently processed by an Artificial Neural Network that acts as the final classifier, enabling the practical deployment of the model as an automated decision-support tool for early melanoma screening in clinical and tele-dermatology environments. The ANN that integrates features from MobileNet + ResNet34 + GoogLeNet models achieved an AUC (94.74%), sensitivity (94.61%), accuracy (98.2%), precision (95.24%), and specificity (99.61%). This result supporting SDG targets related to reducing premature mortality from non-communicable diseases and promoting sustainable health systems. Compared with previously reported single-CNN and hybrid models (accuracy range 81–95%), the proposed framework exhibits a clear improvement of approximately 3–7% in diagnostic accuracy and enhanced stability across minority lesion classes, underscoring its reliability for early melanoma detection and its alignment with SDG targets aimed at reducing premature mortality from non-communicable diseases.</p>

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Fusion of multi CNN features with ANN for early classification of melanoma using dermoscopy images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月11日

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  • Abdullah Shoaib Alshmrani,
  • Fahad M. Alotaibi,
  • Ahmed S. Alfakeeh

摘要

Melanoma presents considerable risks as it is an extremely aggressive type of skin cancer that rapidly metastasize if not addressed promptly. Early detection of melanoma significantly improves treatment outcomes and reduces mortality, aligning with SDG 3 (Good Health and Well-being). Dermatologists face challenges in accurately diagnosing skin lesions and recognizing skin cancer early due to human error and variation in experience. This study develops hybrid Artificial Neural Network (ANN) methodologies integrating CNN-based feature selection algorithms for dermoscopic image analysis, contributing to health system sustainability by enhancing diagnostic accuracy through AI. This approach, tested on the ISIC-2019 dataset, employs advanced filtering and segmentation along with fused multi-model CNN features and Ant Colony Optimization (ACO) for feature selection. The fused features generated from MobileNet, ResNet34, and GoogLeNet are subsequently processed by an Artificial Neural Network that acts as the final classifier, enabling the practical deployment of the model as an automated decision-support tool for early melanoma screening in clinical and tele-dermatology environments. The ANN that integrates features from MobileNet + ResNet34 + GoogLeNet models achieved an AUC (94.74%), sensitivity (94.61%), accuracy (98.2%), precision (95.24%), and specificity (99.61%). This result supporting SDG targets related to reducing premature mortality from non-communicable diseases and promoting sustainable health systems. Compared with previously reported single-CNN and hybrid models (accuracy range 81–95%), the proposed framework exhibits a clear improvement of approximately 3–7% in diagnostic accuracy and enhanced stability across minority lesion classes, underscoring its reliability for early melanoma detection and its alignment with SDG targets aimed at reducing premature mortality from non-communicable diseases.