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Dergiler / Ulusal Travma ve Acil Cerrahi Dergisi / 2022 / Cilt: 28 - Sayı: 2

Use of deep learning methods for hand fracture detection from plain hand radiographs

Kemal ÜRETEN
Hüseyin Fatih SEVİNÇ
Ufuk İĞDELİ
Aslıhan ONAY
Yüksel MARAŞ
Sayfa
196–201
Yayın Tarihi
2022-11-06
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

This CAD method can assist physicians working in the emergency departments of small hospitals when interpreting hand radiographs, especially when it is difficult to reach qualified colleagues, such as night shifts and weekends.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:BACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals.
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Temel Bulgular & Veri:There were 275 fractured wrists, 257 fractured phalanx, and 270 normal hand radiographs in the raw dataset.
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Sonuç & Katkı:This CAD method can assist physicians working in the emergency departments of small hospitals when interpreting hand radiographs, especially when it is difficult to reach qualified colleagues, such as night shifts and weekends.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

BACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are fre quently observed in patients with hand trauma. Here, we aim to develop a computer-aided diagnosis (CAD) method to assist physi cians in the diagnosis of hand fractures using deep learning methods. METHODS: In this study, Convolutional Neural Networks (CNN) were used and the transfer learning method was applied. There were 275 fractured wrists, 257 fractured phalanx, and 270 normal hand radiographs in the raw dataset. CNN, a deep learning method, were used in this study. In order to increase the performance of the model, transfer learning was applied with the pre-trained VGG-16, GoogLeNet, and ResNet-50 networks. RESULTS: The accuracy, sensitivity, specificity, and precision results in Group 1 (wrist fracture and normal hand) dataset were 93.3%, 96.8%, 90.3%, and 89.7% , respectively, with VGG-16, were 88.9%, 94.9%, 84.2%, and 82.4%, respectively, with Resnet-50, and were 88.1%, 90.6%, 85.9%, and 85.3%, respectively, with GoogLeNet. The accuracy, sensitivity, specificity, and precision results in Group 2 (phalanx fracture and normal hand) dataset were 84.0%, 84.1%, 83.8%, and 82.8%, respectively, with VGG-16, were 79.4%, 78.5%, 80.3%, and 79.7%, respectively, with Resnet-50, and were 81.7%, 81.3%, 82.1%, and 81.3%, respectively, with GoogLeNet. CONCLUSION: We achieved promising results in this CAD method, which we developed by applying methods such as transfer learning, data augmentation, which are state-of-the-art practices in deep learning applications. This CAD method can assist physicians working in the emergency departments of small hospitals when interpreting hand radiographs, especially when it is difficult to reach qualified colleagues, such as night shifts and weekends.

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Bu Makaleye Atıf Yap

ÜRETEN, K., SEVİNÇ, H. F., İĞDELİ, U., ONAY, A., MARAŞ, Y. (2022). Use of deep learning methods for hand fracture detection from plain hand radiographs. Ulusal Travma ve Acil Cerrahi Dergisi, Cilt: 28 - Sayı: 2, 196–201. https://ajindex.com/articles/643953

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