| Yazarlar (3) |
Doç. Dr. Kemal AKYOL
Kastamonu Üniversitesi, Türkiye |
Baha Şen
Ankara Yıldırım Beyazıt Üniversitesi, Türkiye |
|
Şafak Bayır
Karabük Üniversitesi, Türkiye |
| Özet |
| Diabetic retinopathy, a subject of many studies in the medical image processing field since long time, is one of the major complications of diabetes mellitus and it cause blindness. In this study, we proposed a method that consist of keypoint detector-feature extraction-reduction process and classifier stages within the framework of hybrid approach for the detection of hard exudates. This method is divided into two parts: learning and querying. In the learning phase, initially we created visual dictionaries for the representation of pathological or non-pathological regions on retinal images. After, we completed modeling process with the training and testing processes. In the querying phase, keypoints and patch images are obtained with keypoint detector algorithm from new retinal images. Thus, knowledge is obtained by these patch images are classified in the final part of this phase. Experimental validation was performed on DIARETDB1 public database. The obtained results are showed us that positive effects of machine learning technique suggested by us for diagnosis of exudate. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale |
| Dergi Adı | International Journal of Computer Applications |
| Dergi ISSN | 0975-8887 |
| Dergi Tarandığı Indeksler | Electronic Journals Library |
| Makale Dili | İngilizce |
| Basım Tarihi | 06-2015 |
| Cilt No | 120 |
| Sayı | 13 |
| Sayfalar | 11 / 16 |
| Doi Numarası | 10.5120/21286-4236 |
| Makale Linki | http://research.ijcaonline.org/volume120/number13/pxc3904236.pdf |
| Atıf Sayıları | |
| Google Scholar | 2 |
