Details

Autor(en) / Beteiligte

• Saha, R
• Chowdhury, A Roy
• Banerjee, S
• Chatterjee, T

Titel

DETECTION OF RETINAL ABNORMALITIES USING MACHINE LEARNING METHODOLOGIES

Ist Teil von

• Neural network world, 2018-01, Vol.28 (5), p.457-471

Ort / Verlag

Prague: Czech Technical University in Prague, Faculty of Transportation Sciences

Erscheinungsjahr

2018

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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 naïve 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.