@inproceedings{290c2696165d4020999a5702661460b5,
title = "Dermatological disease diagnosis using color-skin images",
abstract = "This paper presents an automated dermatological diagnostic system. Etymologically, dermatology is the medical discipline of analysis and treatment of skin anomalies. The system presented is a machine intervention in contrast to human arbitration into the conventional medical personnel based ideology of dermatological diagnosis. The system works on two dependent steps - the first detects skin anomalies and the latter identifies the diseases. The system operates on visual input i.e. high resolution color images and patient history. In terms of machine intervention, the system uses color image processing techniques, k-means clustering and color gradient techniques to identify the diseased skin. For disease classification, the system resorts to feedforward backpropagation artificial neural networks. The system exhibits a diseased skin detection accuracy of 95.99% and disease identification accuracy of 94.016% while tested for a total of 2055 diseased areas in 704 skin images for 6 diseases. {\textcopyright} 2012 IEEE.",
keywords = "Clustering, Color Gradient, Dermatology, GLCM, Skin anomalies",
author = "{Shamsul Arifin}, M. and {Golam Kibria}, M. and Adnan Firoze and {Ashraful Amini}, M. and Hong Yan",
year = "2012",
doi = "10.1109/ICMLC.2012.6359626",
language = "English",
isbn = "9781467314855",
volume = "5",
pages = "1675--1680",
booktitle = "Proceedings - International Conference on Machine Learning and Cybernetics",
note = "2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012 ; Conference date: 15-07-2012 Through 17-07-2012",
}