Skin Cancer Detection and Classification using Deep Learning

Skin cancer is among the most common types of cancer, and quick identification considerably enhances the odds of survival. The purpose of this work is to develop cutting-edge deep learning models that can classify images of skin cells and accurately detect cases of skin cancer. The strength of deep learning algorithms is utilized in this research, which uses a cloud-based architecture. These algorithms, which make up the system's core, allow for the creation of models that significantly improve skin cancer prediction accuracy. The publication provides a thorough explanation of the model construction procedure and how it is used to categorize dermal cell images. Using the ISIC2018 dataset, the convolution neural network (CNN) deep learning technique was employed in this study to identify the two main categories of cancers, malignant and benign. This dataset includes 3297 benign and malignant skin lesions. The deep learning models developed for this study are carefully assessed using validated datasets. Precision, Recall, F1-Score, Support and the area under the curve, all measures, showed remarkable performance at a rate of 96.02. This study presents a model-driven architecture that practitioners can use to quickly diagnose skin cancer using state of the art deep learning architecture.