Open AccessDetection of Skin Diseases Using Matlab
Author(s) -
Ismail Saif Sulaiman Al Shabibi,
Sreedevi Koottala
Publication year - 2020
Publication title -
journal of student research
Language(s) - English
Resource type - Journals
ISSN - 2167-1907
DOI - 10.47611/jsr.vi.884
Subject(s) - computer science , matlab , segmentation , psoriasis , acne , artificial intelligence , software , support vector machine , pixel , skin cancer , feature vector , pattern recognition (psychology) , interface (matter) , computer vision , feature (linguistics) , medicine , dermatology , cancer , linguistics , philosophy , bubble , maximum bubble pressure method , parallel computing , programming language , operating system
It is a great challenge for doctors even with the existence of emerging technology, to diagnose the symptoms of a skin disease. Many people are exposed to serious skin diseases that require them to go to hospitals and go through a number of different expensive medical examinations which takes up to days. The prosed work can solve the above problem to an extent , through the design of a program by MATLAB, a method based on vertical image segmentation, and is proposed to identify three various types of skin diseases, namely, Acne, Cancer and Psoriasis dermatitis. The work is a key to detect a range of symptoms in just a few seconds, making the diagnosis more intuitive and realistic. The aim of this project is the classification of different diseases based on images given as input. The project is based on MATLAB software platform. The images are collected from various publicly available databases Dermnet, DermWeb, etc. Firstly, the sample images of four skin diseases need to be preprocessed. Secondly, the vertical image is segmented and made corresponding geometric transformation. Based on this, three types of skin diseases’ features are extracted, and their correlated parameters of feature texture and pixels of lesion areas are collected through image segmentation. Finally, the symptoms of Acne, Cancer and Psoriasis dermatitis are identified by utilizing the support vector machine (SVM) method in order to improve identification accuracy and provide an interface to doctors.