Otomatik Kemik Kırığı Tespitinde Evrişimsel Sinir Ağları ve Transformer Modellerinin Performansının Analizi

Öz

İnsan varlığı için hayati önem taşıyan iskelet ve kas sisteminin en önemli bileşeni kemiklerdir. Bir kemiğin kırılması belirli bir darbeden veya şiddetli bir geriye doğru hareketten kaynaklanabilir. Bu çalışmada, kemik kırığı tespiti, evrişimli sinir ağı (ESA) tabanlı modeller olan Faster R-CNN ve RetinaNet, ayrıca bir transformer tabanlı model olan DETR (Detection Transformer) kullanılarak gerçekleştirilmiştir. Her model için farklı omurga ağları kullanılarak detaylı bir inceleme yapılmıştır. Bu çalışmanın birincil katkıları, CNN ve transformatör tasarımları arasındaki performans farklılıklarının yöntemsel bir değerlendirmesidir. 5145 görüntüden oluşan açık kaynaklı bir veri setinde eğitilen modeller, 750 test görüntüsünde test edilmiştir. Sonuçlara göre, RetinaNet/ResNet101 modeli diğer modellere göre daha üstün performans sergileyerek 0.901 mAP50 oranına ulaşmıştır. Elde edilen sonuçlar, eğitilen modellerin bilgisayar destekli tanı (BDT) sistemlerinde kullanılabilecek vaat edici sonuçlar sunmaktadır.

Anahtar Kelimeler

• Kemik kırığı
• vision transformer
• DETR
• Faster R-CNN
• RetinaNet

Analyzing the Performance of Convolutional Neural Networks and Transformer Models in Automated Bone Fracture Detection

Öz

The most significant component of the skeletal and muscular system, whose function is vital to human existence, are the bones. Breaking a bone might occur from a specific hit or from a violent rearward movement. In this study, bone fracture detection was performed using convolutional neural network (CNN) based models, Faster R-CNN and RetinaNet, as well as a transformer-based model, DETR (Detection Transformer). A detailed investigation was conducted using different backbone networks for each model. This study's primary contributions are a methodical assessment of the performance variations between CNN and transformer designs. Models trained on an open-source dataset consisting of 5145 images were tested on 750 test images. According to the results, the RetinaNet/ResNet101 model exhibited superior performance with a 0.901 mAP50 ratio compared to other models. The obtained results show promising outcomes that the trained models could be utilized in computer-aided diagnosis (CAD) systems.

Anahtar Kelimeler

• Bone fracture
• vision transformer
• DETR
• Faster R-CNN
• RetinaNet

Kaynakça

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