TY  - JOUR
T1  - Classification of Knee X-rays That Can Be Diagnosed Radiographically Using Deep Learning and Machine Learning Methods
TT  - Radyografik Olarak Tanı Konulabilen Diz Röntgenlerinin Derin Öğrenme ve Makine Öğrenmesi Yöntemleri ile Sınıflandırılması
AU  - Üreten, Kemal
AU  - Duran, Semra
AU  - Maraş, Yüksel
AU  - Atalar, Ebru
AU  - Orhan, Kevser
AU  - Maraş, Hadi Hakan
PY  - 2025
DA  - July
Y2  - 2025
DO  - 10.29130/dubited.1626406
JF  - Duzce University Journal of Science and Technology
JO  - DÜBİTED
PB  - Düzce Üniversitesi
WT  - DergiPark
SN  - 2148-2446
SP  - 1297
EP  - 1308
VL  - 13
IS  - 3
LA  - en
AB  - The aim of this study is to classify knee osteoarthritis, synovial chondromatosis, Osgood-Schlatterdisease, os fabella pathologies that can be diagnosed with plain knee X-rays, and normal kneeradiographs with deep learning and machine learning methods. This study was performed on 540 kneeosteoarthritis, 151 Osgood_Schlatter disease, 191 knee chondromatosis, 152 os fabella and 523 normalknee X-ray images. First, classification was performed with the VGG-16 network, which is a pre-traineddeep learning model. Then, the features extracted with the VGG-16 convolution layer were classifiedwith random forest, support vector machines, logistic regression and decision tree machine learningalgorithms. With VGG-16 model, 95.3% accuracy, 95.1% sensitivity, 98.7% specificity, 96.8%precision, and 95.9% F1 score results were obtained. In classifying the features extracted from the VGG-16 convolution layer with machine learning algorithms, 98.2% accuracy, 99.0% sensitivity, 98.9%specificity, 98.2% precision and 98.5% F1 score results were obtained with the logistic regressionclassifier. In this study, which was conducted to classify radiographically detectable knee pathologies,successful results were obtained with the VGG-16 network. The features extracted from the convolutionlayer of the VGG-16 model were reclassified with machine learning algorithms, logistic regression,support vector machines and random forest classifiers, and improvements in performance metrics wereobtained compared to the VGG-16 model. With this proposed method, the performance of deep learningmodels can be further improved.
KW  - Knee osteoarthritis
KW  - Knee chondromatosis
KW  - Osgood-Schlatter disease
KW  - Os fabella
KW  - Deep
learning
KW  - Machine learning
N2  - Bu çalışmanın amacı, düz diz röntgenleriyle tanısı konulabilen diz osteoartriti, sinovyal kondromatozis,Osgood-Schlatter hastalığı, os fabella patolojileri ve normal diz radyografilerini derin öğrenme vemakine öğrenmesi yöntemleriyle sınıflandırmaktır. Bu çalışma 540 diz osteoartriti, 151Osgood_Schlatter hastalığı, 191 diz kondromatozisi, 152 os fabella ve 523 normal diz röntgen görüntüsüüzerinde gerçekleştirildi. Öncelikle önceden eğitilmiş derin öğrenme modeli olan VGG-16 ağı ilesınıflandırma yapıldı. Daha sonra VGG-16 evrişim katmanı ile çıkarılan özellikler, rastgele orman,destek vektör makineleri, lojistik regresyon ve karar ağacı makine öğrenmesi algoritmalarıylasınıflandırıldı. VGG-16 modeli ile %95,3 doğruluk, %95,1 duyarlılık, %98.7 özgüllük, %96,8 kesinlikve %95,9 F1 skoru sonuçları elde edildi. VGG-16 evrişim katmanından çıkarılan özelliklerin makineöğrenmesi algoritmaları ile sınıflandırılmasında lojistik regresyon sınıflandırıcısı ile %98,2 doğruluk,%99,0 duyarlılık, %98.9 özgüllük, %98,2 kesinlik ve %98,5 F1 skoru sonuçları elde edilmiştir.Radyografik olarak tanısı konulabilen diz patolojilerinin sınıflandırılması amacıyla yapılan buçalışmada, VGG-16 ağı ile başarılı sonuçlar elde edilmiştir. VGG-16 modeli evrişim katmanı üzerindençıkarılan özellikler makine öğrenmesi algoritmaları ile yeniden sınıflandırılmış, lojistik regresyon,destek vektör makineleri ve rastgele orman sınıflandırıcıları ile VGG-16 modeline kıyasla performansmetriklerinde iyileşmeler elde edilmiştir. Önerilen bu yöntemle, derin öğrenme modellerininperformansı daha da iyileştirilebilir.
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UR  - https://doi.org/10.29130/dubited.1626406
L1  - https://dergipark.org.tr/tr/download/article-file/4549821
ER  -