Early Detection of Diabetic Retinopathy with Neural Network
The aim of this paper is to design a computationally intelligent method to determine exudates, the Non-Proliferative Diabetic Retinopathy (NPDR) symptom which is considered to be the initial stage of retinopathy disease. If NPDR is not identified at its earlier stage, it may lead to Proliferative Diabetic Retinopathy (DR), the complicated stage of retinal symptom that may leads to blindness. In this work an automatic computer aided detection system is proposed which screen a large number of people to identify the DR in its earlier stage for proper treatments. In this work, analysis mainly considers three stages which include removal of optic disc and normalization done by histogram processing; texture information extracted using Gray Level Co-Occurrence Matrix (GLCM) and classification is done with the help of Improved Multilayer Perceptron Neural Network (IMPNN) in early stages. Hence the proposed intelligent approaches aid the ophthalmologists with accurate and efficient detection of abnormalities in fundus images. Through this system, the abnormal retinal images can be identified in its initial stage and an accurate assessment of retinal disease is possible.
Author Name: R. Uma Maheswari, M. Lincy Jacquline and Dr.S. Beula Princy
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College: Nirmala College for Women, Coimbatore.
Keywords: Diabetic Retinopathy, Neural Networks, IMPNN, Preprocessing, Deep Learning.