Koroner Arter Hastalığının İris Görüntülerinden Yerel İkili Örüntüler ve Yapay Sinir Ağı Kullanılarak Tahmini

Year 2023, Volume: 13 Issue: 2, 665 - 679, 15.06.2023

Ferdi Özbilgin , Çetin Kurnaz

https://doi.org/10.31466/kfbd.1266996

Cited By: 1

Abstract

Koroner Arter Hastalığı (KAH), kalp kasını besleyen koroner arterlerin daralması veya tıkanması sonucunda oluşan bir kalp hastalığıdır. Dünya genelinde mortalite oranı yüksek bir sağlık sorunu olan KAH’ın erken tanısı çok önemlidir. Bu çalışmada, iridoloji ve görüntü işleme tekniklerinin kullanılarak KAH’ın tahmin edilmesi amaçlanmıştır. Mevcut çalışmalardan farklı olarak iridoloji ile birlikte gerçekleştirilen kalp hastalıkları tahmini çalışmalarında kullanılmamış Yerel İkili Örüntüler (YİÖ) öznitelik çıkarma yönteminin başarımı analiz edilmiştir. Önerilen yöntemde 94 KAH ve 104 Kontrol grubu olmak üzere toplamda 198 gönüllüye ait iris görüntülerinden YİÖ ile öznitelikler çıkarılmış ve Yapay Sinir Ağı (YSA) kullanılarak sınıflandırma gerçekleştirilmiştir. Görüntü içerisinden iris konumlarını bulmak için İntegral Diferansiyel Operatörü ve irisi dikdörtgen formata dönüştürmek için Rubber Sheet Normalizasyon yöntemleri kullanılmıştır. İridoloji haritası vasıtasıyla iriste yer alan kalp bölgesi analiz bölgesi olarak belirlenmiş ve bu bölgeden bir piksel ve sekiz komşulukla YİÖ ile 59 adet histogram temelli öznitelikler çıkarılmıştır. Çıkarılan özniteliklerin YSA ile sınıflandırması gerçekleştirilmiştir. Eğitim ve test olarak iki gruba ayrılan verilerde eğitim işlemi Ölçeklendirilmiş Konjuge Gradyan (Scaled Conjugate Gradient, SCG) algoritması ile gerçekleştirilmiştir. Performans ölçütü olarak belirlenen doğruluk, kesinlik, duyarlılık, özgüllük, F1 skor ve Eğri Altında Kalan Alan (Area Under the Curve, AUC) değerleri test verileri için sırasıyla %91,5, 0,9063, 0,9355, 0,8929, 0,92063 ve 0,9103 olarak bulunmuştur. Elde edilen bulgular doğrultusunda YİÖ temelli önerilen yöntemin KAH’ın tahmin edilmesinde başarılı olduğu söylenebilir.

Keywords

Koroner Arter Hastalığı, İris, İridoloji, Görüntü İşleme, Yerel İkili Örüntüler, Yapay Sinir Ağları

Supporting Institution

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Project Number

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Thanks

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Prediction of Coronary Artery Disease from Iris Images Using Local Binary Patterns and Artificial Neural Network

Abstract

Coronary Artery Disease (CAD) is a heart disease caused by the narrowing or blockage of the coronary arteries that supply the heart muscle. Early diagnosis of CAD, a health problem with a high mortality rate worldwide, is very important. In this study, we aimed to predict CAD using iridology and image processing techniques. Unlike previous studies, the performance of the Local Binary Patterns (LBP) feature extraction method, which has not been utilized in iridology-based heart disease prediction studies, was analyzed. In the proposed method, features were extracted with LBP from iris images of a total of 198 volunteers (94 CAD and 104 Control group), and classification was performed using Artificial Neural Network (ANN). The Integral Differential Operator method was used to find the iris positions in the image, and Rubber Sheet Normalization was used to convert the iris into a rectangular format. Through the iridology map, the heart region in the iris was determined as the analysis region, and 59 histogram-based features were extracted from this region with one pixel and eight neighborhoods with the LBP. The classification was performed using ANN with the extracted features. The data were divided into two groups: training and test. The Scaled Conjugate Gradient (SCG) algorithm performed the training process. The accuracy, precision, sensitivity, specificity, F1 score and Area Under the Curve (AUC) values determined as performance criteria were 91.5%, 0.9063, 0.9355, 0.8929, 0.92063 and 0.9103 for the test data, respectively. Based on the findings, it can be said that the proposed method based on the LBP is successful in predicting CAD.

Keywords

Coronary Artery Disease, Iris, Iridology, Image Processing, Local Binary Patterns, Artificial Neural Networks

Project Number

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