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

TL;DR

This paper demonstrates that transfer learning with VGG16 and VGG19 models significantly improves skin cancer classification accuracy, achieving over 98% accuracy without data augmentation.

Contribution

It introduces a transfer learning framework combining VGG16 and VGG19 architectures for skin lesion classification, enhancing accuracy with minimal data preprocessing.

Findings

Achieved 98.18% classification accuracy.

Improved accuracy by 3% over existing methods.

Validated model effectiveness with K-fold cross validation.

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.