Investigation of Binary and Multiclass Classification Performance of Skin Cancer Images Using Transfer Learning Methods

This study demonstrated the effectiveness of transfer learning models combined with pre-processing techniques and hyperparameter tuning for skin cancer classification. Both classification tasks showed significant performance improvements using these methods. The VGG16 model achieved the highest accuracy in both scenarios, highlighting its potential for further development in dermoscopy systems to assist dermatologists in diagnosing skin cancer. Future research should explore a broader range of datasets and refine pre-processing techniques.