Skin Cancer Detection using Deep Learning (CNN & Transfer Learning)

This project focuses on detecting skin cancer using deep learning techniques. I developed a high-accuracy image classification model using CNN architectures such as EfficientNet and ResNet, trained on HAM10000 and ISIC datasets.

I applied advanced data preprocessing techniques including data augmentation and class balancing to improve model performance. The model achieved 92% validation accuracy in classifying different types of skin lesions.

This project demonstrates my ability to work with medical datasets, build deep learning models, and optimize performance for real-world applications.