Mugla Journal of Science and Technology 2149-3596 Mugla Sitki Kocman University 10.22531/muglajsci.269964 Engineering Mühendislik DETECTION OF HARD EXUDATES IN DIABETIC RETINOPATHY RETINAL IMAGES BY UTILIZING VISUAL DICTIONARY AND CLASSIFIER APPROACHES GÖRSEL SÖZLÜK VE SINIFLANDIRMA YAKLAŞIMLARINDAN FAYDALANARAK DİYABETİK RETİNOPATİLİ RETİNAL GÖRÜNTÜLERDE SERT EKSUDALARIN TESPİTİ Akyol Kemal KASTAMONU ÜNİVERSİTESİ Bayır Şafak Şen Baha 06 08 2016 2 1 1 6 09 26 2015 12 29 2015 Copyright © 2015, Mugla Journal of Science and Technology 2015 Mugla Journal of Science and Technology

Diabetic retinopathy is a disease that causes blindness resulting from damages that emerge in the retina depending on the diabetes mellitus. There are two stages of the disease including the non-proliferative and proliferative. Eyesight loss is blocked by means of early detection and diagnosis of non-proliferative DR findings. In this study, we designed a decision support system for automatic detection of hard exudates which are early stage DR lesions. This system consists of region-of-interest, feature extraction, visual dictionary and classifying stages. We tested the performance of the system, which we carried out based on system learning and analysis of new retinal images, on the public DIARETDB1 retinal image dataset. Experimental results showed us that machine learning technique suggested by us is successful.

Diyabetik retinopati,şeker hastalığına bağlı olarak retinada ortaya çıkan hasarlanmaların sonucukörlüğe neden olan bir hastalıktır. Bu hastalığın erken evre (nonproliferatif)veileri evre (proliferative) olmak üzere iki aşaması vardır. Erken evreDRbulgularının erken tanı ve teşhisi sayesinde görme kaybının önüne geçilir. Buçalışmamızda erken evre DR lezyonlarından olan sert eksuda bölgelerininotomatik olarak tespiti için bir karar destek sistemi tasarladık. Bu sistem, anahtarnokta çıkarımı, özellik çıkarımı, görsel sözlük ve sınıflandırma aşamalarınıiçerir. Sistemin öğrenmesi ve yeni retinal görüntülerin analizi temelinedayanarak gerçekleştirdiğimiz bu sistemin performansını publik (herkese açık)DIARETDB1 retinal görüntü dataseti üzerinde test ettik. Yapay Sinir Ağları,Rastgele Orman ve Karar Ağacı algoritmaları ile elde ettiğimiz deneyselsonuçlar önerdiğimiz makina öğrenmesi tekniğinin başarılı olduğunu bizegöstermiştir.

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