Speed Efficient Fast Fourier Transform for Signal Processing of Nucleotides to Detect Diabetic Retinopathy Using Machine Learning

Author:

Saravanakumar C.1,Usha Bhanu N.1

Affiliation:

1. Department of Electronics and Communication Engineering, SRM Valliammai Engineering College, Kattankulathur 603203, India

Abstract

Diabetic Retinopathy (DR) is a complicated disease of diabetes, which specifically affects the retina. The human-intensive analysis mechanism of DR infected retina are likely to diagnose wrongly compared to computer-intensive diagnosis systems. In this paper, in order to aid the computer based approach for the diagnosis of DR, a model based on machine learning algorithm is proposed. The nucleotides of the human retina are processed with the help of signal processing methodologies. A speed efficient Fast Fourier transform is proposed to work out the FFT of huge amount of samples with higher pace. The improvement in speed is achieved in 98% of the samples. The prediction parameters, derived from these samples are utilized to classify the healthy retina sequence and an infected retina. In this study, Fine Tree, KNN Fine, Weighted KNN, Ensemble Bagged Trees and Ensemble Subspace KNN classifiers are employed to build the models. The simulated results using MATLAB software show that the accuracy is 98% which is better than image processing based methods which were used earlier. The performance parameters such as sensitivity and specificity are determined for each model. The faithfulness of the model is studied by deriving the ROC Curve.

Publisher

American Scientific Publishers

Subject

Health Informatics,Radiology, Nuclear Medicine and imaging

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3