Detection and classification of femoral neck fracture using YOLOv8
YOLOv8 kullanarak femoral boyun kırığının tespiti ve sınıflandırılması
- Tez No: 900134
- Danışmanlar: PROF. DR. RAFET AKDENİZ
- Tez Türü: Yüksek Lisans
- Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2024
- Dil: İngilizce
- Üniversite: İstanbul Aydın Üniversitesi
- Enstitü: Lisansüstü Eğitim Enstitüsü
- Ana Bilim Dalı: Bilgisayar Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Bilgisayar Mühendisliği Bilim Dalı
- Sayfa Sayısı: Belirtilmemiş.
Özet
Femoral neck fractures are considered to be one of the most challenging orthopedic conditions because of the technical difficulties in the management of these injuries and possible complications, including nonunion and avascular necrosis. These fractures are common in elderly patients and they mostly occur due to low energy trauma such as falls. Early diagnosis and identification of femoral neck fractures are critical to proper clinical management and in reducing complications. This study employs the YOLOv8 model, which is a recent advancement in object detection, to detect and classify femoral neck fractures in X-ray images. The YOLOv8 model shows impressive results, with the mean Average Precision mAP50 of 97. 9%, a precision of 93. 5%, and a mAP50-95 of 62. 5%. Our proposed system encompasses several stages: data collecting, data preprocessing, model training, model validation, and model deployment. In the process of data preprocessing several data augmentation methods was performed to improve the model's resilience. The YOLOv8 model was then trained with this dataset and further rigorous testing conducted to determine the efficiency of the model. The results show that the proposed model has great potential for the automatic detection and classification of femoral neck fractures, which will be helpful for radiologists. When implemented in clinical environments, this system may increase diagnostic accuracy, decrease the workload, and consequently, benefit patients.
Özet (Çeviri)
Femoral neck fractures are considered to be one of the most challenging orthopedic conditions because of the technical difficulties in the management of these injuries and possible complications, including nonunion and avascular necrosis. These fractures are common in elderly patients and they mostly occur due to low energy trauma such as falls. Early diagnosis and identification of femoral neck fractures are critical to proper clinical management and in reducing complications. This study employs the YOLOv8 model, which is a recent advancement in object detection, to detect and classify femoral neck fractures in X-ray images. The YOLOv8 model shows impressive results, with the mean Average Precision mAP50 of 97. 9%, a precision of 93. 5%, and a mAP50-95 of 62. 5%. Our proposed system encompasses several stages: data collecting, data preprocessing, model training, model validation, and model deployment. In the process of data preprocessing several data augmentation methods was performed to improve the model's resilience. The YOLOv8 model was then trained with this dataset and further rigorous testing conducted to determine the efficiency of the model. The results show that the proposed model has great potential for the automatic detection and classification of femoral neck fractures, which will be helpful for radiologists. When implemented in clinical environments, this system may increase diagnostic accuracy, decrease the workload, and consequently, benefit patients.
Benzer Tezler
- Acil serviste proksimal femur fraktürlerinin tespitinde yapay zekanın kullanımı
The use of artificial intelligence in the detection of proximal femur fractures in the emergency department
EKREM BUĞRA GÖKÇEK
Tıpta Uzmanlık
Türkçe
2025
Acil TıpAfyonkarahisar Sağlık Bilimleri ÜniversitesiAcil Tıp Ana Bilim Dalı
DOÇ. DR. AYŞE ERTEKİN
- Derin öğrenme teknikleri kullanılarak kemik kırığı tespiti ve sınıflandırması
Bone fracture detection and classification using deep learning techniques
KORAY AÇICI
Doktora
Türkçe
2021
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolBaşkent ÜniversitesiBilgisayar Mühendisliği Ana Bilim Dalı
DR. ÖĞR. ÜYESİ EMRE SÜMER
- Femur boyun kırıklarında internal fixasyon
Internal fixation of femoral neck fractures
MEHMET GEM
Tıpta Uzmanlık
Türkçe
2010
Ortopedi ve TravmatolojiDicle ÜniversitesiOrtopedi ve Travmatoloji Ana Bilim Dalı
PROF. DR. AHMET KAPUKAKAYA
- Travma hastalarına çekilen abdominal tomografi görüntülerinin acil tıp asistanı tarafından akut travma patolojilerini değerlendirme düzeyi
The level of evaluation of acute trauma pathologies by the emergency medicine assistant of abdominal tomography images taken in trauma patients
MEHMET SOYUGÜZEL
Tıpta Uzmanlık
Türkçe
2022
Acil TıpAfyonkarahisar Sağlık Bilimleri ÜniversitesiAcil Tıp Ana Bilim Dalı
YRD. DOÇ. DR. AYŞE ERTEKİN
- Kalça radyografilerinde derin öğrenme yöntemleri ile proksimal femur kırıklarının tanısı ve sınıflandırılması
Diagnosis and classification of proximal femur fractures on hip radiographs with deep learning methods
HASAN ERSÖNMEZ
Yüksek Lisans
Türkçe
2022
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolÇankırı Karatekin ÜniversitesiElektrik-Elektronik Mühendisliği Ana Bilim Dalı
PROF. DR. MURAT ARI
DR. ÖĞR. ÜYESİ SEDA ŞAHİN