TRANSFER LEARNING‐BASED CLASSIFICATION OF KNEE OSTEOARTHRITIS SEVERITY FROM X-RAY IMAGES

Abstract

Knee osteoarthritis (KOA) a degenerative, long-term joint condition that, more often than not, affects the elderly and is characterized by articular cartilage degradation. Appropriate treatment and early analysis are essential for sickness control. However, traditional diagnostic methods for classifying KOA from X-ray images require laborious expertise and, unfortunately, have a large margin of error. This study presents an image processing-based solution for multi-classification KOA severity from X-ray images using the Bilateral filter, contrast-limited adaptive histogram equalization (CLAHE), and transfer learning models. The CLAHE method improved image quality, while the Bilateral filter enhanced details and minimized blurriness in X-ray images. KOA image dataset consists of 9786 knee images and five class labels. The performances of transfer learning models including AlexNet, ResNet101, DenseNet201, and VGG19 were compared. The ResNet101 model emerged as the most effective, achieving a kappa statistic of 0.970, weighted F1-score of 0.978, and an overall accuracy of 97.85%. This model’s high accuracy and precision make it a dependable and objective diagnostic solution.

Keywords

• Deep Learning
• Image Processing
• Transfer Learning
• ResNet101
• Knee Osteoarthritis.

DIZ OSTEOARTRITI ŞIDDETININ X-RAY GÖRÜNTÜLERINDEN TRANSFER ÖĞRENME TABANLI SINIFLANDIRILMASI

Abstract

Diz osteoartriti (KOA), çoğunlukla yaşlıları etkileyen ve eklem kıkırdağı bozulmasıyla karakterize dejeneratif, uzun vadeli bir eklem durumudur. Hastalık kontrolü için uygun tedavi ve erken analiz kritiktir. Bununla birlikte, X-ray görüntülerinden KOA sınıflandırması için geleneksel tanı yöntemleri uzmanlık gerektirmektedir, yorucudur ve maalesef büyük bir hata payına sahiptir. Bu çalışma, Bilateral filtresi, kontrast sınırlı adaptif histogram eşitleme (CLAHE) ve transfer öğrenme modelleri kullanarak X-ray görüntülerinden KOA şiddetini sınıflandırmak için görüntü işleme tabanlı bir çözüm sunmaktadır. CLAHE yöntemi görüntü kalitesini iyileştirirken, Bilateral filtresi X-ray görüntülerindeki ayrıntıları iyileştirerek bulanıklığı en aza indirmiştir. KOA görüntü veri seti 9786 diz görüntüsü ve beş sınıf etiketinden oluşmaktadır. AlexNet, ResNet101, DenseNet201 ve VGG19 dahil olmak üzere transfer öğrenme modellerinin performansları karşılaştırıldı. ResNet101 modeli 0,970 kappa istatistiği, 0,978 ağırlıklı F1-skoru ve %97,85 genel doğruluk elde ederek en etkili model olarak ortaya çıkmıştır. Bu modelin yüksek doğruluğu ve kesinliği onu güvenilir ve objektif bir tanı çözümü olduğunu göstermektedir.

Keywords

• Derin Öğrenme
• Görüntü İşleme
• Transfer Öğrenme
• ResNet101
• Diz Osteoartriti

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