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Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models

Amir Faghihi, Mohammadreza Fathollahi, Roozbeh Rajabi

TL;DR

This study tackles automated melanoma vs. benign skin lesion classification using transfer learning with a novel fusion of VGG16 and VGG19 pre-trained weights incorporated into a customized AlexNet architecture. By freezing early layers, adding task-specific layers, and training on ISIC/MED-NODE data with dropout, the approach achieves high accuracy, reaching 97.5–98.1% in 10-fold cross-validation. Thorough evaluation includes ablation studies, optimizer comparisons (Adam vs SGD), and early stopping, demonstrating robustness and efficiency. The findings highlight the practical potential of tailored transfer-learning configurations for dermatology screening, particularly when training data are limited.

Abstract

Today, skin cancer is considered as one of the most dangerous and common cancers in the world which demands special attention. Skin cancer may be developed in different types; including melanoma, actinic keratosis, basal cell carcinoma, squamous cell carcinoma, and Merkel cell carcinoma. Among them, melanoma is more unpredictable. Melanoma cancer can be diagnosed at early stages increasing the possibility of disease treatment. Automatic classification of skin lesions is a challenging task due to diverse forms and grades of the disease, demanding the requirement of novel methods implementation. Deep convolution neural networks (CNN) have shown an excellent potential for data and image classification. In this article, we inspect skin lesion classification problem using CNN techniques. Remarkably, we present that prominent classification accuracy of lesion detection can be obtained by proper designing and applying of transfer learning framework on pre-trained neural networks, without any requirement for data enlargement procedures i.e. merging VGG16 and VGG19 architectures pre-trained by a generic dataset with modified AlexNet network, and then, fine-tuned by a subject-specific dataset containing dermatology images. The convolution neural network was trained using 2541 images and, in particular, dropout was used to prevent the network from overfitting. Finally, the validity of the model was checked by applying the K-fold cross validation method. The proposed model increased classification accuracy by 3% (from 94.2% to 98.18%) in comparison with other methods.

Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models

TL;DR

This study tackles automated melanoma vs. benign skin lesion classification using transfer learning with a novel fusion of VGG16 and VGG19 pre-trained weights incorporated into a customized AlexNet architecture. By freezing early layers, adding task-specific layers, and training on ISIC/MED-NODE data with dropout, the approach achieves high accuracy, reaching 97.5–98.1% in 10-fold cross-validation. Thorough evaluation includes ablation studies, optimizer comparisons (Adam vs SGD), and early stopping, demonstrating robustness and efficiency. The findings highlight the practical potential of tailored transfer-learning configurations for dermatology screening, particularly when training data are limited.

Abstract

Today, skin cancer is considered as one of the most dangerous and common cancers in the world which demands special attention. Skin cancer may be developed in different types; including melanoma, actinic keratosis, basal cell carcinoma, squamous cell carcinoma, and Merkel cell carcinoma. Among them, melanoma is more unpredictable. Melanoma cancer can be diagnosed at early stages increasing the possibility of disease treatment. Automatic classification of skin lesions is a challenging task due to diverse forms and grades of the disease, demanding the requirement of novel methods implementation. Deep convolution neural networks (CNN) have shown an excellent potential for data and image classification. In this article, we inspect skin lesion classification problem using CNN techniques. Remarkably, we present that prominent classification accuracy of lesion detection can be obtained by proper designing and applying of transfer learning framework on pre-trained neural networks, without any requirement for data enlargement procedures i.e. merging VGG16 and VGG19 architectures pre-trained by a generic dataset with modified AlexNet network, and then, fine-tuned by a subject-specific dataset containing dermatology images. The convolution neural network was trained using 2541 images and, in particular, dropout was used to prevent the network from overfitting. Finally, the validity of the model was checked by applying the K-fold cross validation method. The proposed model increased classification accuracy by 3% (from 94.2% to 98.18%) in comparison with other methods.
Paper Structure (11 sections, 8 figures, 2 tables)

This paper contains 11 sections, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Convolution Neural Network (CNN)
  4. Model architecture
  5. Dataset
  6. Results and Discussion
  7. Ablation Study
  8. Optimizer Selection
  9. K-fold Cross Validation
  10. Early stopping
  11. Conclusion

Figures (8)

  • Figure 1: A view of the customized CNN artichecture.
  • Figure 2: Proposed transfer learning to Customize CNN
  • Figure 3: The first row shows some examples of melanoma lesions, and the second row some examples of harmless moles
  • Figure 4: A comparison chart of Transfer Learning changes based on VGG16, Green: Model performance using normal transfer learning, Red: Model performance using modified transfer learning
  • Figure 5: A comparison chart of Transfer Learning changes based on VGG19, Green: Model performance using normal transfer learning, Blue: Model performance using modified transfer learning
  • ...and 3 more figures