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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
XLESCIENCE
2025-06-01
|
| Series: | International Journal of Advances in Signal and Image Sciences |
| Subjects: | |
| Online Access: | https://xlescience.org/index.php/IJASIS/article/view/276 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849709204945764352 |
|---|---|
| author | Maysaa R. Naeemah Mohammed Y. Kamil |
| author_facet | Maysaa R. Naeemah Mohammed Y. Kamil |
| author_sort | Maysaa R. Naeemah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5c7540d50c0e4dfb9a5ba3961f818e83 |
| institution | DOAJ |
| issn | 2457-0370 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | XLESCIENCE |
| record_format | Article |
| series | International Journal of Advances in Signal and Image Sciences |
| spelling | doaj-art-5c7540d50c0e4dfb9a5ba3961f818e832025-08-20T03:15:23ZengXLESCIENCEInternational Journal of Advances in Signal and Image Sciences2457-03702025-06-0111116917910.29284/ijasis.11.1.2025.169-179304SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATIONMaysaa R. NaeemahMohammed Y. KamilSkin 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.https://xlescience.org/index.php/IJASIS/article/view/276deep learning, transfer learning, skin cancer, ai in healthcare, medical imaging. |
| spellingShingle | Maysaa R. Naeemah Mohammed Y. Kamil SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATION International Journal of Advances in Signal and Image Sciences deep learning, transfer learning, skin cancer, ai in healthcare, medical imaging. |
| title | SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATION |
| title_full | SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATION |
| title_fullStr | SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATION |
| title_full_unstemmed | SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATION |
| title_short | SKINVGG-NET: A MODIFIED AND FINE-TUNED VGG19-BASED DEEP LEARNING ARCHITECTURE FOR SKIN CANCER CLASSIFICATION |
| title_sort | skinvgg net a modified and fine tuned vgg19 based deep learning architecture for skin cancer classification |
| topic | deep learning, transfer learning, skin cancer, ai in healthcare, medical imaging. |
| url | https://xlescience.org/index.php/IJASIS/article/view/276 |
| work_keys_str_mv | AT maysaarnaeemah skinvggnetamodifiedandfinetunedvgg19baseddeeplearningarchitectureforskincancerclassification AT mohammedykamil skinvggnetamodifiedandfinetunedvgg19baseddeeplearningarchitectureforskincancerclassification |