Detection of Choroidal Neovascularization Through Characterization of Changes in Anterior and Posterior Eye Segments

Anitha, G.; Ismail, M. Mohamed

doi:10.1007/s13319-018-0189-4

Cited by 1 publication

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“…Using image processing techniques, factors such as skewness, Euler number, area, thickness, and its standard deviation and maximum thickness are obtained (Prashanth et al, 2010;Anitha and Ismail, 2018). After extracting these features, it came to observe that there are significant changes in the above factors during CNV which allows us to identify the presence of CNV (Spaide, 2009).…”

Section: Introductionmentioning

confidence: 99%

Identification and characterisation of choroidal neovascularisation using e-Health data through an optimal classifier

Anitha

Ismail

Lakshmanaprabu

2020

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Over the years, health informatics and eHealth gained more popularity in health care application. The collection of eHealth data becomes easier due to the advancement of digital technology. In this paper, the e-Health based supporting system is developed for the classification of a retinal disease called CNV. CNV is a retinal disease caused due to the growth of abnormal blood vessels in the choroidal layer. A good classifier for CNV data makes the process of identifying the disease easier and it will help the medical practitioners to give the treatment at the right time. A comparison has been done among different machine learning classifiers such as support vector machine (SVM), k-nearest neighbours (kNN), neural network (NN), ensemble and naive Bayes classifiers and they are tested and evaluated based on accuracy and training time. From the results, it is observed that kNN classifier outperforms the other classifiers in all aspects.

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Section: Introductionmentioning

confidence: 99%