Skin Cancer Detection and Classification using Convolutional Neural Network

Skin cancer is a form of cancer that originates in skin tissue and can lead to harm, disability, and even death by affecting the surrounding tissue. Early and accurate diagnosis, followed by prompt treatment, are essential to minimize and manage the adverse consequences of skin cancer. Physicians often spend significant time distinguishing between skin cancer and benign tumor lesions due to their similar appearances. In this study, a system was developed to automatically detect skin cancer using Convolutional Neural Networks (CNNs). The proposed model employs various optimizers, including the highly effective Adam optimizer. Notably, the Adam optimizer achieved an impressive accuracy rate of 94% in identifying skin cancer from the dataset