X-ışını görüntülerinden omuz implantlarının tespiti ve sınıflandırılması: YOLO ve önceden eğitilmiş evrişimsel sinir ağı tabanlı bir yaklaşım.

Shoulder implants must be replaced after a certain period of time. But determining the manufacturer or model of the implant during this change is often a mistake-prone and difficult process for medical experts. The aim of this study is to identify 4 different implant manufacturers from 597 shoulder implant X-ray images. For this purpose, both pre-trained CNN architectures (DenseNet201, DenseNet169, InceptionV3, NasNetLarge, VGG16, VGG19 and Resnet50) and cascade models that feed these architectures with YOLOv3 detection algorithm were created and the classification performances of these models were compared. The task of the YOLOv3 detection algorithm in cascade models is to detect the head area of the shoulder implants and give this area as an input to CNN architectures. In addition, traditional machine learning methods were combined with the ensemble learning method and their performance on the data set was revealed. The highest classification performance was achieved in the cascade DenseNet201 model with an accuracy rate of 84.76%. This rate is higher in the literature than in a different study using similar dataset. Classification accuracy of ensemble models is substantially lower than CNN models. Also, the classification accuracy of YOLO supported cascade models is higher than individual CNN models. That is, focusing on the head of the implant with the YOLOV3 detection algorithm has increased the classification accuracy. This method will inspire future studies in this field. [ABSTRACT FROM AUTHOR]