Comparative Evaluation of CNN Architectures for Skin Cancer Classification

Skin cancer is one of the fastest-growing health problems worldwide. Early and accurate diagnosis is essential for improving treatment success and patient survival. However, many previous studies have focused on single CNN architectures or limited datasets, resulting in models with restricted generalizability. To address this gap, this study presents a comparative evaluation of three deep learning architectures (DenseNet169, MobileNetV2, and VGG19) for automatic classification of benign and malignant skin cancers using dermoscopic digital images. A total of 10,000 images were compiled from three public Kaggle datasets, preprocessed through resizing and data augmentation, and trained using transfer learning based on ImageNet weights. Two data split schemes (60:20:20 and 80:10:10) were applied to assess model robustness. Experimental results show that DenseNet169 achieved the highest test accuracy of 90.7 per cent, while MobileNetV2 was the fastest with an inference time of 16 seconds. These findings highlight the tradeoff between accuracy and computational efficiency and support the use of deep learning models, particularly DenseNet169 and MobileNetV2, in the development of real-time AI-assisted skin cancer diagnostic systems.