SKIN DISEASE DETECTION USING DEEP LEARNING

Abstract

Dermatological disorders are one of the most widespread diseases in the world. Despite being

common its diagnosis is extremely difficult because of its complexities of skin tone, color, presence of hair. This paper provides an approach to use various computer vision based techniques

(deep learning) to automatically predict the various kinds of skin diseases. The system consists of three phases- The feature extraction phase, the training phase and the testing / validation phase. The system makes use of deep learning technology to train itself with the various skin images. The main objective of this system is to achieve maximum accuracy of skin disease prediction. This work focuses on skin disease prediction using deep learning techniques. For the implementation, the dataset used for the skin disease prediction is the ISIC dataset with 8 category of skin diseases. For the classification, the deep learning algorithm used is ResNeT50. Restnet50 is a pretrained network, which can be used for the detection of skin disease detection using the concept of transfer learning. For that the neural network extracts image features from the skin images. The extracted features are used for disease detection for 8 classes of skin diseases.