Using deep learning for detecting gender in adult chest radiographs

Zhiyun Xue; Sameer Antani; L. Rodney Long; George R. Thoma

doi:10.1117/12.2293027

6 March 2018 Using deep learning for detecting gender in adult chest radiographs

Zhiyun Xue, Sameer Antani, L. Rodney Long, George R. Thoma

Proceedings Volume 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications; 105790D (2018) https://doi.org/10.1117/12.2293027
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

In this paper, we present a method for automatically identifying the gender of an imaged person using their frontal chest x-ray images. Our work is motivated by the need to determine missing gender information in some datasets. The proposed method employs the technique of convolutional neural network (CNN) based deep learning and transfer learning to overcome the challenge of developing handcrafted features in limited data. Specifically, the method consists of four main steps: pre-processing, CNN feature extractor, feature selection, and classifier. The method is tested on a combined dataset obtained from several sources with varying acquisition quality resulting in different pre-processing steps that are applied for each. For feature extraction, we tested and compared four CNN architectures, viz., AlexNet, VggNet, GoogLeNet, and ResNet. We applied a feature selection technique, since the feature length is larger than the number of images. Two popular classifiers: SVM and Random Forest, are used and compared. We evaluated the classification performance by cross-validation and used seven performance measures. The best performer is the VggNet-16 feature extractor with the SVM classifier, with accuracy of 86.6% and ROC Area being 0.932 for 5-fold cross validation. We also discuss several misclassified cases and describe future work for performance improvement.

Citation Download Citation

Zhiyun Xue, Sameer Antani, L. Rodney Long, and George R. Thoma "Using deep learning for detecting gender in adult chest radiographs", Proc. SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, 105790D (6 March 2018); https://doi.org/10.1117/12.2293027

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