模式分析与应用(Pattern Analysis and Applications)杂志介绍了新模式分析技术以及工业和医学应用的原始研究。它详细介绍了模式识别和分析在应用领域的新技术和方法,包括计算机视觉和图像处理、语音分析、机器人技术、多媒体、文档分析、字符识别、模式识别知识工程、分形分析和智能控制。模式分析与应用(PAA)也检查了高级方法的使用,包括统计技术、神经网络、遗传算法、模糊模式识别、机器学习和硬件实现,这些都与模式分析作为一个研究领域的发展或新的模式分析应用的细节相关。 官网地址:http://dblp.uni-trier.de/db/journals/paa/

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Wrist fractures are common cases in hospitals, particularly in emergency services. Physicians need images from various medical devices, and patients medical history and physical examination to diagnose these fractures correctly and apply proper treatment. This study aims to perform fracture detection using deep learning on wrist Xray images to assist physicians not specialized in the field, working in emergency services in particular, in diagnosis of fractures. For this purpose, 20 different detection procedures were performed using deep learning based object detection models on dataset of wrist Xray images obtained from Gazi University Hospital. DCN, Dynamic R_CNN, Faster R_CNN, FSAF, Libra R_CNN, PAA, RetinaNet, RegNet and SABL deep learning based object detection models with various backbones were used herein. To further improve detection procedures in the study, 5 different ensemble models were developed, which were later used to reform an ensemble model to develop a detection model unique to our study, titled wrist fracture detection combo (WFD_C). Based on detection of 26 different fractures in total, the highest result of detection was 0.8639 average precision (AP50) in WFD_C model developed. This study is supported by Huawei Turkey R&D Center within the scope of the ongoing cooperation project coded 071813 among Gazi University, Huawei and Medskor.

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Wrist fractures are common cases in hospitals, particularly in emergency services. Physicians need images from various medical devices, and patients medical history and physical examination to diagnose these fractures correctly and apply proper treatment. This study aims to perform fracture detection using deep learning on wrist Xray images to assist physicians not specialized in the field, working in emergency services in particular, in diagnosis of fractures. For this purpose, 20 different detection procedures were performed using deep learning based object detection models on dataset of wrist Xray images obtained from Gazi University Hospital. DCN, Dynamic R_CNN, Faster R_CNN, FSAF, Libra R_CNN, PAA, RetinaNet, RegNet and SABL deep learning based object detection models with various backbones were used herein. To further improve detection procedures in the study, 5 different ensemble models were developed, which were later used to reform an ensemble model to develop a detection model unique to our study, titled wrist fracture detection combo (WFD_C). Based on detection of 26 different fractures in total, the highest result of detection was 0.8639 average precision (AP50) in WFD_C model developed. This study is supported by Huawei Turkey R&D Center within the scope of the ongoing cooperation project coded 071813 among Gazi University, Huawei and Medskor.

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Wrist fractures are common cases in hospitals, particularly in emergency services. Physicians need images from various medical devices, and patients medical history and physical examination to diagnose these fractures correctly and apply proper treatment. This study aims to perform fracture detection using deep learning on wrist Xray images to assist physicians not specialized in the field, working in emergency services in particular, in diagnosis of fractures. For this purpose, 20 different detection procedures were performed using deep learning based object detection models on dataset of wrist Xray images obtained from Gazi University Hospital. DCN, Dynamic R_CNN, Faster R_CNN, FSAF, Libra R_CNN, PAA, RetinaNet, RegNet and SABL deep learning based object detection models with various backbones were used herein. To further improve detection procedures in the study, 5 different ensemble models were developed, which were later used to reform an ensemble model to develop a detection model unique to our study, titled wrist fracture detection combo (WFD_C). Based on detection of 26 different fractures in total, the highest result of detection was 0.8639 average precision (AP50) in WFD_C model developed. This study is supported by Huawei Turkey R&D Center within the scope of the ongoing cooperation project coded 071813 among Gazi University, Huawei and Medskor.

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