Open AccessUnhealthy skin analyzer for mobile platform using Canny Edge Detection and Similarity Score
Author(s) -
Z. Zulhelmi,
Zulfikar Zulfikar,
Teuku Yuliar Arif,
Afdhal Afdhal,
Putra Nasri Syawaldi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1502/1/012056
Subject(s) - similarity (geometry) , computer science , enhanced data rates for gsm evolution , process (computing) , canny edge detector , edge detection , artificial intelligence , image (mathematics) , computer vision , simple (philosophy) , spectrum analyzer , pattern recognition (psychology) , data mining , image processing , telecommunications , operating system , philosophy , epistemology
The increasing use of mobile platforms for detecting a disease challenge the researcher for designing a more efficient application than previously. Skin diseases can be recognized visually by an expert. However, the diseases are often ignored, especially by they who live in the remote area. For this reason, it requires a system worked solely independent of medic expert through a simple device like a smartphone. The study designs a system for detecting the healthiness of skin through its image. The proposed design is the improved model of the previously published work. The system requires suspected unhealthy and reference images. Both images will be compared after passing the edge process. The result is displayed on the platform containing a suggestion for the user to have medical treatment. For detecting the skin diseases accurately, an adjustment in the edge process and comparing process (similarity check) is required. Based on the experiment, a suitable threshold for edge detection are 0 for low and 50 for high. Regarding the edge process, a suitable similarity threshold will be estimated. The similarity threshold that suits to the designed application is 6. Lastly, the decision making to determine the healthiness of an image is 45.35%.