Skin cancer is one of the most prevalent cancer types worldwide currently, underscoring the significance of early detection and precise diagnosis for effective treatment. This study employs the HAM10000 dataset, comprising 10015 skin lesion instances across seven categories of pigmented skin lesions. Preprocessing techniques are applied, including image resizing and normalization, and data augmentation is implemented to address dataset imbalances. The research primarily employs supervised machine learning models for skin cancer detection, utilizing Convolutional Neural Networks (CNNs). Specifically, VGG16, VGG19, ResNet50, MobileNet, MobileNetV2, and MobileNetV3 are examined for their performance on the dataset. Results indicate that ResNet50, with 92.31% accuracy and 91.98% F1-score, demonstrates higher performance, while MobileNetV3, with about 13 minutes of training time, outperforms in terms of computational efficiency.
