DİYABETİK RETİNOPATİ TESPİTİ İÇİN ATOM ARAMA OPTİMİZASYONU İLE ÖZELLİK SEÇİMİ YÖNTEMİ

Diyabetik Retinopati (DR), dünya genelinde milyonlarca insanı etkileyen görme kaybı ve körlüğün başlıca nedenidir. DR tespiti için retinal fundus görüntülerini kullanan birçok farklı bilimsel ve tıbbi yaklaşımlar bulunmaktadır. Bu yaklaşımların çoğunda, özellik seçimi aşaması yapılmadan diyabetik retinopati veri kümelerine çeşitli makine öğrenimi ve derin öğrenme yaklaşımları uygulanmıştır. Çalışmada UCI makine öğrenmesi deposundan elde edilen DR veri kümesi kullanılmıştır. Bu makalede, atom dinamiklerinden faydalanılarak önerilmiş popülasyon temelli yeni bir metasezgisel yöntem olan Atom Arama Optimizasyon (AAO) algoritması, ilk kez DR veri kümesi için bir özellik seçim yöntemi olarak kullanılmıştır. Normalize edilen veri kümesine AAO algoritmasının uygulanmasının ardından elde edilen yeni veri kümesi Bagging, CvR, IBk, JRip, Kstar ve SimpleCart olmak üzere altı sınıflandırma algoritması ile test edilmiştir. Aynı sınıflandırma algoritmaları, orijinal DR veri kümesine de uygulanmıştır. Elde edilen sonuçlar AAO algoritması ile özellik seçimi yapılmış veri kümesi ile karşılaştırılmıştır. Önerilen modelin performansı doğruluk, duyarlılık, özgüllük, kesinlik, f-ölçütü ve roc alanı değerleri bakımından değerlendirilmiştir. Elde edilen sonuçlar, AAO algoritması ile özellik seçimi yapılmış veri kümesi üzerinde Bagging, CvR, IBk, JRip, Kstar ve SimpleCart algoritmaları ile daha iyi değerler elde edildiğini göstermektedir. Bu bakımdan önerilen özellik seçimi ile algoritmaların özellik seçimi olmadan elde edilen sınıflandırma oranlarında doğruluk için ortalama %2.7, duyarlılık için %3.5, özgüllük için %2’lik bir artış sağlanmıştır.

Anahtar Kelimeler:

Atom arama optimizasyon algoritması, Diyabetik Retinopati, Özellik seçimi, Sınıflandırma

FEATURE SELECTION METHOD WITH ATOM SEARCH OPTIMIZATION FOR DETECTION OF DIABETIC RETINOPATHY

Diabetic Retinopathy (DR) is the leading cause of vision loss and blindness, affecting millions of people worldwide. There are many different scientific and medical approaches that use retinal fundus images for DR detection. In most of these approaches, various machine learning and deep learning approaches have been applied to diabetic retinopathy datasets without the feature selection step. The DR dataset obtained from the UCI machine learning repository was used in the study. In this article, Atom Search Optimization (ASO) algorithm, a new population-based metaheuristic method proposed by utilizing atom dynamics, is used for the first time as a feature selection method for the DR dataset. Applied the ASO algorithm to the normalized dataset, the new dataset was tested by six classification algorithms: Bagging, CvR, Ibk, JRip, Kstar, and SimpleCart. The same classification algorithms were applied to the original DR dataset. The results obtained were compared with the data set that was selected with the ASO algorithm. The performance of the proposed model was evaluated in terms of accuracy, sensitivity, specificity, precision, f-measure, and roc curve values. The results show that better values were obtained with Bagging, CvR, Ibk, JRip, Kstar, and SimpleCart algorithms on the dataset selected with the ASO algorithm. In this regard, an increase of 2.7% for the average accuracy, 3.5% for the sensitivity, and 2% for the specificity were achieved in the classification rates obtained without feature selection of the algorithms with the proposed feature selection.

Keywords:

Atom search optimization algorithm, Diabetic Retinopathy, Feature selection, Classification,

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