Skin Cancer Classification using Tensorflow and Keras

Abstract

Skin cancer is an abnormal growth of skin cells. It generally develops in areas that are exposed to the sun, but it can also form in places that don’t normally get sun exposure. Skin cancers aren’t all identical, and they may not cause many symptoms. Still, unusual changes to your skin can be a warning sign for the different types of cancer. Being alert for changes to your skin may help you get a diagnosis earlier. Accurate and precise diagnosis of diseases has been a significant challenge and he recent advances in computer vision made possible by deep learning has paved the way for disease diagnosis for skin cancer. It described the innovative solution that provides efficient disease detection and deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various skin cancer diseases. A variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available skin cancer disease given image dataset. So, it observed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. And this model to deploy Django web framework.