Accelerated and precise skin cancer detection through an enhanced machine learning pipeline for improved diagnostic accuracy

Unrepaired DNA damage in skin cells causes mutations leading to skin cancer, a highly aggressive malignancy. This study proposes a machine learning (ML)-based framework for accurate and automated skin cancer detection, integrating EfficientNetV2L for advanced feature extraction and LightGBM (LGBM) for gradient boosting. The ensemble model effectively classifies benign and malignant skin lesions, leveraging EfficientNetV2L's feature extraction capabilities and LGBM's computational efficiency. A dataset comprising 3,297 images of benign and malignant skin cancer classes was used for training. Data augmentation techniques were applied to enhance dataset reliability. The proposed training pipeline, optimized for…