Classification of Knee X-rays That Can Be Diagnosed Radiographically Using Deep Learning and Machine Learning Methods

Yıl 2025, Cilt: 13 Sayı: 3, 1297 - 1308, 31.07.2025

Kemal Üreten , Semra Duran , Yüksel Maraş , Ebru Atalar , Kevser Orhan , Hadi Hakan Maraş

https://doi.org/10.29130/dubited.1626406

Öz

The aim of this study is to classify knee osteoarthritis, synovial chondromatosis, Osgood-Schlatter
disease, os fabella pathologies that can be diagnosed with plain knee X-rays, and normal knee
radiographs with deep learning and machine learning methods. This study was performed on 540 knee
osteoarthritis, 151 Osgood_Schlatter disease, 191 knee chondromatosis, 152 os fabella and 523 normal
knee X-ray images. First, classification was performed with the VGG-16 network, which is a pre-trained
deep learning model. Then, the features extracted with the VGG-16 convolution layer were classified
with random forest, support vector machines, logistic regression and decision tree machine learning
algorithms. 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 regression
classifier. 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 convolution
layer 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 were
obtained compared to the VGG-16 model. With this proposed method, the performance of deep learning
models can be further improved.

Anahtar Kelimeler

Knee osteoarthritis , Knee chondromatosis , Osgood-Schlatter disease , Os fabella , Deep learning , Machine learning

Etik Beyan

ETHICS APPROVAL: An ethical approval certificate was obtained from the local ethic commitee, Ankara Bilkent City Hospital, Clinical Research Ethics Committee No. 1, date : 20.04.2022, no : E1-22-2420

Destekleyen Kurum

Funding None

Radyografik Olarak Tanı Konulabilen Diz Röntgenlerinin Derin Öğrenme ve Makine Öğrenmesi Yöntemleri ile Sınıflandırılması

Öz

Bu çalışmanın amacı, düz diz röntgenleriyle tanısı konulabilen diz osteoartriti, sinovyal kondromatozis,
Osgood-Schlatter hastalığı, os fabella patolojileri ve normal diz radyografilerini derin öğrenme ve
makine öğrenmesi yöntemleriyle sınıflandırmaktır. Bu çalışma 540 diz osteoartriti, 151
Osgood_Schlatter hastalığı, 191 diz kondromatozisi, 152 os fabella ve 523 normal diz röntgen görüntüsü
üzerinde gerçekleştirildi. Öncelikle önceden eğitilmiş derin öğrenme modeli olan VGG-16 ağı ile
sınıflandırma yapıldı. Daha sonra VGG-16 evrişim katmanı ile çıkarılan özellikler, rastgele orman,
destek vektör makineleri, lojistik regresyon ve karar ağacı makine öğrenmesi algoritmalarıyla
sınıflandırıldı. VGG-16 modeli ile %95,3 doğruluk, %95,1 duyarlılık, %98.7 özgüllük, %96,8 kesinlik
ve %95,9 F1 skoru sonuçları elde edildi. VGG-16 evrişim katmanından çıkarılan özelliklerin makine
öğrenmesi 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 bu
çalışmada, VGG-16 ağı ile başarılı sonuçlar elde edilmiştir. VGG-16 modeli evrişim katmanı üzerinden
çıkarılan özellikler makine öğrenmesi algoritmaları ile yeniden sınıflandırılmış, lojistik regresyon,
destek vektör makineleri ve rastgele orman sınıflandırıcıları ile VGG-16 modeline kıyasla performans
metriklerinde iyileşmeler elde edilmiştir. Önerilen bu yöntemle, derin öğrenme modellerinin
performansı daha da iyileştirilebilir.

Anahtar Kelimeler

Diz osteoartriti , Diz kondromatozu , Osgood-Schlatter hastalığı , Os fabella , Derin öğrenme , Makine öğrenmesi

Kaynakça

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