All-star approach to a small medical imaging dataset: combined deep,transfer,and classical machine learning approaches for the determination of radial head fractures

Abstract

Background

Radial head fractures are often evaluated in emergency departments and can easily be missed. Automated or semi-automated detection methods that help physicians may be valuable regarding the high miss rate.

Purpose

To evaluate the accuracy of combined deep, transfer, and classical machine learning approaches on a small dataset for determination of radial head fractures.

Material and Methods

A total of 48 patients with radial head fracture and 56 patients without fracture on elbow radiographs were retrospectively evaluated. The input images were obtained by cropping anteroposterior elbow radiographs around a center-point on the radial head. For fracture determination, an algorithm based on feature extraction using distinct prototypes of pretrained networks (VGG16, ResNet50, InceptionV3, MobileNetV2) representing four different approaches was developed. Reduction of feature space dimensions, feeding the most relevant features, and development of ensemble of classifiers were utilized.

Results

The algorithm with the best performance consisted of preprocessing the input, computation of global maximum and global mean outputs of four distinct pretrained networks, dimensionality reduction by applying univariate and ensemble feature selectors, and applying Support Vector Machines and Random Forest classifiers to the transformed and reduced dataset. A maximum accuracy of 90% with MobileNetV2 pretrained features was reached for fracture determination with a small sample size.

Conclusion

Radial head fractures can be determined with a combined approach and limitations of the small sample size can be overcome by utilizing pretrained deep networks with classical machine learning methods.

Keywords

Deep learning radial head fracture classical machine learning transfer learning

Get full access to this article

View all access options for this article.

References

Pappas

Bernstein

. Fractures in brief: radial head fractures. Clin Orthop Relat Res 2010;468:914–916.

Roidis

Papadakis

Karachalios

, et al. Radial head fractures. In: Mirzayan

Itamura

JM,

editors. Shoulder and Elbow Trauma. New York, NY: Thieme Medical Publishers Inc, 2004:22–35.

Pavić

Margetić

Hnatešen

. Diagnosis of occult radial head and neck fracture in adults. Injury 2015;46:S119–S124.

Thian

Jagmohan

, et al. Convolutional neural networks for automated fracture detection and localization on wrist radiographs. Radiol Artif Intell 2019;1:e180001.

Parakh

Lee

, et al. Urinary stone detection on CT images using deep convolutional neural networks: evaluation of model performance and generalization. Radiol Artif Intell 2019;1:e180066.

Chea

Mandell

. Current applications and future directions of deep learning in musculoskeletal radiology. Skeletal Radiol 2020;49:183–197.

Simonyan

Zisserman

. Very deep convolutional networks for large-scale image recognition. 2015. arXiv:1409.1556v6 cs.CV. Available at: https://arxiv.org/abs/1409.1556v6 (last accessed December 2019).

Krizhevsky

Sutskever

Hinton

. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems – Volume 1, 2012, pp. 1097–1105. Red Hook, NY: Curran Associates Inc.

Zhang

Ren

, et al. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. Las Vegas, NV: The Institute of Electrical and Electronics Engineers Inc.

10.

Howard

Zhu

Chen

, et al. MobileNets: efficient convolutional neural networks for mobile vision applications. 2017. arXiv:1704.04861v1 cs.CV. Available at: https://arxiv.org/abs/1704.04861 (last accessed December 2019).

11.

Rajpurkar

Irvin

Bagul

, et al. MURA: large dataset for abnormality detection in musculoskeletal radiographs. 2018. arXiv:1712.06957v4 physics.med-ph. Available at: https://arxiv.org/abs/1712.06957 (last accessed December 2019).

12.

Kim

MacKinnon

. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol 2018;73:439–445.

13.

Tomita

Cheung

Hassanpour

. Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 2018;98:8–15.

14.

Lindsey

Daluiski

Chopra

, et al. Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci USA 2018;115:11591–11596.

15.

Olczak

Fahlberg

Maki

, et al. Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures? Acta Orthop 2017;88:581–586.

16.

Urakawa

Tanaka

Goto

, et al. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal Radiol 2019;48:239–244.

17.

Cheng

Lee

, et al. Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs. Eur Radiol 2019;29:5469–5477.

18.

Langerhuizen

DWG

Janssen

Mallee

, et al. What are the applications and limitations of artificial intelligence for fracture detection and classification in orthopaedic trauma imaging? A systematic review. Clin Orthop Relat Res 2019;477:2482–2491.

19.

Kitamura

Chung

Moore

. Ankle fracture detection utilizing a convolutional neural network ensemble implemented with a small sample, de novo training, and multiview incorporation. J Digit Imaging 2019;32:672–677.

20.

Tajbakhsh

Shin

Gurudu

, et al. Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 2016;35:1299–1312.

21.

Deng

Dong

Socher

, et al. Imagenet: a large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2019:248–255.

22.

Wang

. Support Vector Machines: Theory and Applications. The Nederlands: Springer-Verlag Berlin Heidelberg, 2005.

23.

Liaw

Wiener

. Classification and regression by random forest. R News 2002;2:18–22.

24.

Hernandez-Garcia

Konig

. Data augmentation instead of explicit regularization. 2019. arXiv:1806.03852v4 cs.CV. Available at: https://arxiv.org/abs/1806.03852 (last accessed December 2019).

25.

Gan

Lin

, et al. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop 2019;90:394–400.

26.

Anu

Mallikarjunaswamy

Raman

. Detection of bone fracture using image processing methods. IJCA Proceedings on National Conference on Power Systems and Industrial Automation NCPSIA, 2015, pp. 6–9.

27.

Rayan

Reddy

Kan

, et al. Binomial classification of pediatric elbow fractures using a deep learning multiview approach emulating radiologist decision making. Radiol Artif Intell 2019;1:e180015.

28.

Chada

. Machine learning models for abnormality detection in musculoskeletal radiographs. Reports 2019;2:26.

29.

Cao

Wang

Moradi

, et al. Fracture detection in x-ray images through stacked random forests feature fusion. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, NY, 2015, pp. 801–805.

30.

Chung

Han

Lee

, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop 2018;89:468–473.

31.

Onan

Korukoglu

. A feature selection model based on genetic rank aggregation for text sentiment classification. J Inf Sci 2017;43:25–38.

32.

Onan

. An ensemble scheme based on language function analysis and feature engineering for text genre classification. J Inf Sci 2018;44:28–47.