Detection of skin cancer using deep neural networks

Abstract

Skin cancer is a huge issue which gets neglected very often. Sometimes the human eye is unable to precisely detect diseases from imaging data, in cases of doctor's manual inspection. In this age, we see the rise of use of deep learning methods in our daily life problem solving. Therefore, we develop an automated computerised system for detecting skin diseases using deep neural network algorithms. In the proposed model, we have used several neural network algorithms and analyse their performances to detect five major skin diseases and Figure out the best performing algorithm in terms of accuracy. CNN and by using Keras Sequential API, we have structured a new model to gainan accuracy of around 80%. Later, for comparison and also to increase accuracy we have used architectures that use pre-trained data. These transfer learning model includes VGG11, RESNET50 and DENSENET121. Among the algorithms used in the proposed models, resnet architecture achieve highest accuracy of 90%. © 2019 IEEE.