Transfer Learning with Enhanced Models for Skin Cancer Detection: A Comprehensive Evaluation of Transfer Learning and Data Augmentation on the ISIC 2020 Dataset

 Skin cancer, encompassing melanoma and non-melanoma variants, remains a prevalent global ma lignancy, necessitating timely detection to enhance patient outcomes. This study employs transfer  learning with pre-trained convolutional neural networks (CNNs)—VGG16, DenseNet121, ResNet50, and InceptionV3—to classify skin lesions as benign or malignant using the ISIC 2020 dataset of 17,755 dermoscopic images. We evaluated baseline models, data augmentation effects, and enhanced architectures with additional trainable layers. Enhanced DenseNet121 achieved superior performance, with 96.45% accuracy, 96.32% precision, 96.69% recall, and a 96.50% F1-score. Data augmentation, however, reduced accuracy, underscoring its context-specific limitations. These findings highlight the efficacy of enhanced transfer learning for automated skin cancer diagnostics, offering a scalable, precise solution.