Dermatological disease detection using image processing and machine learning

Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which...

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Bibliographic Details
Published in2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) pp. 1 - 6
Main Authors Kumar, Vinayshekhar Bannihatti, Kumar, Sujay S., Saboo, Varun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
Artificial neural networks
Automated Disease Diagnosis
Computational Intelligence
Computational modeling
Computer vision
Data Mining
Dermatology
Diseases
Feature extraction
Image color analysis
Image Processing
Machine Learning
Skin
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Summary:Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines Computer Vision and Machine Learning on clinically evaluated histopathological attributes to accurately identify the disease. In the first stage, the image of the skin disease is subject to various kinds of pre-processing techniques followed by feature extraction. The second stage involves the use of Machine learning algorithms to identify diseases based on the histopathological attributes observed on analysing of the skin. Upon training and testing for the six diseases, the system produced an accuracy of up to 95 percent.
DOI:10.1109/ICAIPR.2016.7585217