SKIN CANCER DETECTION USING DEEP LEARNING ALGORITHMS CNN

Abstract

Skin cancer is a serious problem that is frequently ignored. Sometimes, when a doctor does a manual examination, the human eye cannot accurately identify illnesses from imaging data. Deep learning techniques are increasingly being used in today's world to solve problems in our daily lives. Thus, we use deep neural network methods to create an automated computerized system for identifying skin conditions. We employed a number of neural network algorithms in the suggested model, analyzed their results, and determined which algorithm performed the best in terms of accuracy in detecting the five main skin conditions. CNN, and we have developed a new model to achieve an accuracy of about 80% by utilizing the Keras Sequential API. Later, to improve accuracy and for comparison, we have employed pre-trained data-based architectures. These deep learning models consist of DENSENET201 andinception-v3. The ResNet architecture achieves the maximum accuracy of 97% among the algorithms employed in the suggested models.