SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATION
Skin cancer, one of the most common and potentially fatal cancers, requires early and correct diagnosis to improve patient outcomes. While dermatologists possess extensive diagnostic expertise, recent studies have shown that Convolutional Neural Networks (CNNs) can often surpass human performance in...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
XLESCIENCE
2025-06-01
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| Series: | International Journal of Advances in Signal and Image Sciences |
| Subjects: | |
| Online Access: | https://xlescience.org/index.php/IJASIS/article/view/276 |
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| Summary: | Skin cancer, one of the most common and potentially fatal cancers, requires early and correct diagnosis to improve patient outcomes. While dermatologists possess extensive diagnostic expertise, recent studies have shown that Convolutional Neural Networks (CNNs) can often surpass human performance in multiclass skin lesion classification, owing to their ability to extract subtle and complex features that may be overlooked during manual examination. In this study, SkinVGG-Net, an enhanced deep learning framework based on the VGG19 architecture is proposed. The SkinVGG-Net uses transfer learning and an advanced fine-tuning strategy, along with a comprehensive data preprocessing pipeline that includes image normalization, resizing, and extensive data augmentation to address the class imbalance challenges. The SkinVGG-Net model achieves 90.95% accuracy on HAM10000 dataset, with high performance across seven types of skin cancer. These results demonstrate the capability of CNN-based systems to provide consistent, accurate diagnostic support and highlight their promise as assistive tools in clinical settings with greater precision than traditional visual assessment methods. |
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| ISSN: | 2457-0370 |