Optimizing Skin Cancer Detection Using Transfer Learning and Image Augmentation

This project aims to detect skin cancer using various transfer learning models, including VGG16, VGG19, DenseNet121, and EfficientNetB7. It compares the effectiveness of pre-trained ImageNet weights versus training from scratch (None) to evaluate model performance. The study also examines how image augmentation affects model generalization and accuracy. By integrating deep learning architectures with data augmentation techniques, the objective is to improve the precision and reliability of skin cancer detection. This AI-driven approach seeks to offer an effective solution for early diagnosis and treatment, contributing to advancements in healthcare technology for skin cancer screening.