Open AccessClassification of multiple diseases based on wavelet features
Author(s) -
Bodasingi Nalini,
Balaji Narayanam
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2016.0171
Subject(s) - support vector machine , computer science , pattern recognition (psychology) , artificial intelligence , classifier (uml) , wavelet , artificial neural network , graphical user interface , machine learning , programming language
This study presents an efficient disease classification approach based on medical images. The approach is more efficient as it reduces the computational complexity. The implementation uses only two wavelet filters in selecting the texture features as compared with five filters used in the earlier research works. The computed average and energy features are fed to feed‐forward neural network (FFNN) and support vector machine (SVM) classifiers. The SVM is proved as a better classifier than the FFNN for all the three diseases related to skin, breast and retina with an improved accuracies of 89%, 92% and 100%, respectively. Also, a graphical user interface is developed useful for various disease classification based on the whole dataset of size 100.