Abstract—Skin diseases are a common health problem worldwide; this article proposes a method based on deep learning techniques combined with computer vision to detect various types of dermatological diseases. The system relies on m-health, a fundamental component of e-health, which involves the use of mobile devices for diagnosis, thus making it completely non-invasive for the patient and therefore accessible in rural areas where access to dermatologists is limited. Image processing algorithms have been used in the system for the extraction of characteristics of the sample provided by the patient, which serves to feed the convolutional neural network, this network allows to classify images by subdividing them into layers, making it easier to extract patterns through the application of different filters. This expert system works in two phases: the first: analysis and processing of the color image to extract the characteristics and patterns to obtain classified models and then make the prediction or identification of the disease. The second phase of retraining consists of a feedback to the training data of the network, which allows automatic learning of the algorithm. The system successfully detects three types of dermatological diseases: Dermatitis, Pityriasis or Tinea versicolor and Melasma, diseases with the highest incidence in Ecuador, with an average accuracy rate of 90%.
Index Terms—Skin diseases, deep learning, computer vision, m-health, convolutional neural network, image processing, automatic learning.
The authors are with the Universidad de las Fuerzas Armadas ESPE, Ecuador (e-mail: kpmalla@espe.edu.ec, evicente@espe.edu.ec, fjmontaluisa@espe.edu.ec).
Cite: Malla Zhunio Katherine Patricia, Vicente Zapata Edison Santiago, and Montaluisa Yugla Franklin Javier, "Expert System for the Pre-diagnosis of Skin Diseases," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 81-86, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).