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

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Titel: Classification of Knee X-rays That Can Be Diagnosed Radiographically Using Deep Learning and Machine Learning Methods.
Alternate Title: Radyografik Olarak Tanı Konulabilen Diz Röntgenlerinin Derin Öğrenme ve Makine Öğrenmesi Yöntemleri ile Sınıflandırılması. (Turkish)
Autoren: ÜRETEN, Kemal, DURAN, Semra, MARAŞ, Yüksel, ATALAR, Ebru, ORHAN, Kevser, MARAŞ, Hadi Hakan
Quelle: Duzce University Journal of Science & Technology; tem2025, Vol. 13 Issue 3, p1297-1308, 12p
Schlagwörter: MACHINE learning, KNEE osteoarthritis, SUPPORT vector machines, RANDOM forest algorithms, X-ray imaging, DEEP learning
Abstract (English): 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 pretrained 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. [ABSTRACT FROM AUTHOR]
Abstract (Turkish): 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. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract: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 pretrained 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. [ABSTRACT FROM AUTHOR]
ISSN:21482446
DOI:10.29130/dubited.1626406