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Using deep learning for detecting gender in adult chest radiographs

机译：使用深度学习检测成人胸部X光片中的性别

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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.
机译：在本文中，我们提出了一种使用前额胸部X射线图像自动识别被成像人员性别的方法。我们需要确定某些数据集中缺少性别信息的动机。所提出的方法利用基于卷积神经网络（CNN）的深度学习和转移学习技术来克服在有限数据中开发手工特征的挑战。具体来说，该方法包括四个主要步骤：预处理，CNN特征提取器，特征选择和分类器。该方法在从多个来源获得的，具有不同采集质量的组合数据集上进行了测试，从而导致对每个方法应用了不同的预处理步骤。对于特征提取，我们测试并比较了四种CNN架构，即AlexNet，VggNet，GoogLeNet和ResNet。由于特征长度大于图像数量，因此我们应用了特征选择技术。使用和比较了两个流行的分类器：SVM和随机森林。我们通过交叉验证评估了分类性能，并使用了七个性能指标。性能最佳的是带有SVM分类器的VggNet-16特征提取器，其准确度为86.6％，ROC面积为0.932，可进行5倍交叉验证。我们还讨论了一些错误分类的案例，并描述了未来的性能改进工作。

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