Detection of retinal abnormalities using machine learning methodologies

Title:

Detection of retinal abnormalities using machine learning methodologies

Creator:

Saha, Rituparna , Roy Chowdhury, Amrita , Banerjee, Sreeparna , and Chatterjee, Tamojit

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:213b3957-9ec4-4e4a-8e0d-f6dde6aecafe
uuid:213b3957-9ec4-4e4a-8e0d-f6dde6aecafe
doi:10.14311/NNW.2018.28.025

Subject:

diabetic retinopathy, age related macular degeneration, multilevel thresholding, perceptron, and support vector machine

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

This paper presents an algorithm for the design of a computer aided diagnosis system to detect, quantify and classify the lesions of non-proliferative diabetic retinopathy as well as dry age related macular degeneration from the fundus retina images. Symptoms of non-proliferative diabetic retinopathy in images consist of bright lesions like hard exudates, cotton wool spots and dark lesions like microaneurysms, hemorrhages. Dry age related macular degeneration is manifested as a bright lesion called drusen. The proposed system consists of two parts: image processing, where preprocessed gray scale images are segmented to extract candidate lesions using a combination of Gaussian filtering and multilevel thresholding followed by classification of the different lesions in non-proliferative diabetic retinopathy and age related macular degeneration using perceptron, support vector machine and naive Bayes classifier. From the comparative performance analysis of the classification techniques, it is observed that comparable results are obtained from single layer perceptron and support vector machine and they both outperform naive Bayes classifier. The classification accuracy of support vector machine classifier for dark lesion class is 97.13% and the classification accuracy of single layer perceptron for bright lesion class is 95.13% with optimal feature set.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2018 Volume:28 | Number:5

Harvested from:

CDK

Metadata only:

false