Derma-Speziale: An Image-Based Automated System for Skin Disease Identification Using Convolutional Neural Networks
Neethu C Sekhar, Rosmin Augustine , Johny Xavier Fernandez , Midhlaj Ahammed T K, Serene Anna Giji
Deep Learning, prediction modelling, CNN, Tensorflow
Skin disease is perhaps the most well-known kind of human illness, which may happen to everybody regardless of any demographic characteristics and skin diseases are becoming one of the most common health issues in all countries worldwide. Multiple tests should be carried out to determine the skin diseases faced by patients. This takes a while, depending on the prediction of the diagnosis. As a result, a framework is required that can analyse skin diseases without any of these requirements and provide superior results in seconds. An automated image-based system based on convolutional neural networks (CNN) for skin disease recognition is proposed in this paper. Training dataset is required for various skin diseases.. The dataset includes all forms of skin diseases, however we focused on nine main types of skin diseases, with each class including between 150 and 300 samples. Users can enter images and system processes, use CNN algorithm to extract features, and use softmax classifier to diagnose diseases. The proposed CNN model is compared with a recurrent neural networks(RNN) model to ensure CNN model is more accurate in classification and prediction of the input images.
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Unique Paper ID: 154162

Publication Volume & Issue: Volume 8, Issue 7

Page(s): 1 - 6
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