Advancing Skin Cancer Detection: Leveraging Deep Learning for Early Diagnosis

This research presents a novel approach for early skin cancer detection using a deep learning model based on DenseNet 201. The study utilizes the ISIC dataset, consisting of 1900 normal and 1597 cancerous images, to train and evaluate the model. The proposed methodology involves data loading, categorical labeling, normalization, and model building using Keras with TensorFlow backend. The balanced composition of the dataset ensures accurate evaluation, resulting in an impressive accuracy of 92.8%. The research highlights the significance of early skin cancer detection and the potential of deep learning models to improve diagnostic accuracy. The integration of DenseNet 201 demonstrates the effectiveness of this approach in classifying skin cancer images.