| Yazarlar (3) |
Doç. Dr. Kemal AKYOL
Kastamonu Üniversitesi, Türkiye |
Şafak Bayır
Karabük Üniversitesi, Türkiye |
|
Baha Şen
Ankara Yıldırım Beyazıt Üniversitesi, Türkiye |
| Özet |
| Retina is a network layer containing light-sensitive cells. Diseases that occur in this layer, which performs the eyesight, threaten our eye-sight directly. Diabetic Retinopathy is one of the main complications of diabetes mellitus and it is the most significant factor contributing to blindness in the later stages of the disease. Therefore, early diagnosis is of great importance to prevent the progress of this disease. For this purpose, in this study, an application based on image processing techniques and machine learning, which provides decision support to specialist, was developed for the detection of hard exudates, cotton spots, hemorrhage and microaneurysm lesions which appear in the early stages of the disease. The meaningful information was extracted from a set of samples obtained from the DIARETDB1 dataset during the system modeling process. In this process, Gabor and Discrete Fourier Transform attributes were utilized and dimension reduction was performed by using Spectral Regression Discriminant Analysis algorithm. Then, Random Forest and Logistic Regression and classifier algorithms’ performances were evaluated on each attribute dataset. Experimental results were obtained using the retinal fundus images provided from both DIARETDB1 dataset and the department of Ophthalmology, Ataturk Training and Research Hospital in Ankara. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | ESCI dergilerinde yayınlanan tam makale |
| Dergi Adı | International Journal of Advanced Computer Science and Applications |
| Dergi ISSN | 2156-5570 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | ESCI: Emerging Sources Citation Index |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2017 |
| Cilt No | 8 |
| Sayı | 12 |
| Sayfalar | 369 / 379 |
| Doi Numarası | 10.14569/IJACSA.2017.081249 |
| Atıf Sayıları | |
| Google Scholar | 8 |
