Classification of multiple diseases based on wavelet features

The Journal of Engineering

Narayanam Balaji¹  Nalini Bodasingi¹ 

[1] Department of ECE, JNTUK University College of Engineering, Vizianagaram, Andhra Pradesh 535003, India

关键词: computed average features;    feedforward neural network;    wavelet filters;    graphical user interface;    computational complexity;    FFNN;    SVM;    skin;    breast;    multiple disease classification;    medical images;    texture feature selection;    dataset size;    support vector machine classifiers;    retina;    energy features;    wavelet features;   

DOI  :  10.1049/joe.2016.0171

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

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.

【 授权许可】

CC BY