Bel Omurgası Dejeneratif Hastalıklarının Derin Öğrenme Teknikleri ile Sınıflandırılması

Öz

Bu çalışma, bel omurgası dejeneratif hastalıklarının sınıflandırılması için derin öğrenme (DL) tekniklerinin kullanımını incelemektedir. Özellikle çalışmada spinal kanal stenozunun tespitinde kullanılan Manyetik Rezonans (MR) görüntüleri değerlendirilmiştir. Derin öğrenme modellerinin, radyolojik görüntüler üzerinde otomatik analiz yeteneği sayesinde tanı süreçlerini hızlandırma potansiyeli ortaya konulmuştur. Çalışmada, farklı derin öğrenme modelleri kullanılmış ancak en düşük kayıp değeri EfficientNetV2-Large mimarisi ile elde edilmiştir. Gelişmiş veri artırma teknikleri, özellikle nadir görülen vakalar için hedefli yaklaşımlar ve yüksek çözünürlüklü (512x512) görüntülerle çalışılması, modelin performansını önemli ölçüde artırmıştır. Mimari güncellemeler ve veri işleme stratejileri sonucunda, test log-loss değeri en düşük 0.69 seviyesine düşürülmüştür. Ayrıca, farklı modellerin tahminlerinin ensemble learning (topluluk öğrenmesi) çerçevesinde soft voting yöntemiyle bir araya getirilmesiyle elde edilen sonuçlar da sunulmuştur. Bu yaklaşım, public test veri setinde 0.604510 gibi düşük bir log-loss değeri elde etmiştir. Sonuçlar, modelin klinik açıdan kritik olan "severe" vakaları ayırt etme yeteneğini ve genişletilmiş sınıf yapısında dahi genelleme gücünü koruduğunu göstermektedir.

Anahtar Kelimeler

• Dejeneratif disk hastalığı
• tıbbi görüntüleme
• Data Augmentation
• CNN
• Ensemble Learning.

Classification of Lumbar Spine Degenerative Diseases Using Deep Learning Techniques

Abstract

This study investigates the use of deep learning (DL) techniques for the classification of lumbar spine degenerative diseases. In particular, Magnetic Resonance (MR) images used for the detection of spinal canal stenosis are evaluated. The potential of deep learning models to accelerate diagnostic processes through their capability for automatic analysis of radiological images is demonstrated. Various deep learning models were employed in the study; however, the lowest loss value was achieved with the EfficientNetV2-Large architecture. Advanced data augmentation techniques, especially targeted approaches for rare cases, and the use of high-resolution (512x512) images significantly improved the model's performance. As a result of architectural updates and data processing strategies, the test log-loss value was reduced to as low as 0.69. Additionally, the results obtained by combining the predictions of different models through ensemble learning with a soft voting method are also presented. This approach yielded a low log-loss value of 0.604510 on the public test dataset. The results demonstrate that the model is capable of distinguishing clinically critical "severe" cases and maintains its generalization ability even in an expanded class structure.

Keywords

• Degenerative disc disease
• medical imaging
• Data Augmentation
• CNN
• Ensemble Learning.

Kaynakça

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