USE OF CONVOLUTIONAL NEURAL NETWORKS FOR X-RAY IMAGE ORIENTATION DETERMINATION

Sandi Baressi Baressi Šegota, S. Lysdahlgaard, S. Hess, R. Antulov
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Abstract

The fact that Artificial Intelligence (AI) based algorithms exhibit a high performance on image classification tasks has been shown many times. Still, certain issues exist with the application of machine learning (ML) artificial neural network (ANN) algorithms. The best known is the need for a large amount of statistically varied data, which can be addressed with expanded collection or data augmentation. Other issues are also present. Convolutional neural networks (CNNs) show extremely high performance on image-shaped data. Despite their performance, CNNs exhibit a large issue which is the sensitivity to image orientation. Previous research shows that varying the orientation of images may greatly lower the performance of the trained CNN. This is especially problematic in certain applications, such as X-ray radiography, an example of which is presented here. Previous research shows that the performance of CNNs is higher when used on images in a single orientation (left or right), as opposed to the combination of both. This means that the data needs to be differentiated before it enters the classification model. In this paper, the CNN-based model for differentiation between left and right-oriented images is presented. Multiple CNNs are trained and tested, with the highest performing being the VGG16 architecture which achieved an Accuracy of 0.99 (+/- 0.01), and an AUC of 0.98 (+/- 0.01). These results show that CNNs can be used to address the issue of orientation sensitivity by splitting the data in advance of being used in classification models.
使用卷积神经网络确定x射线图像的方向
基于人工智能(AI)的算法在图像分类任务中表现出高性能的事实已被多次证明。尽管如此,机器学习(ML)人工神经网络(ANN)算法的应用仍存在一些问题。最著名的是需要大量的统计变化数据,这可以通过扩大收集或增加数据来解决。其他问题也存在。卷积神经网络(cnn)在图像形状数据上表现出极高的性能。尽管它们的性能,cnn表现出一个很大的问题,即对图像方向的敏感性。先前的研究表明,改变图像的方向可能会大大降低训练后的CNN的性能。这在某些应用中尤其成问题,例如x射线照相,这里给出了一个例子。先前的研究表明,cnn在单一方向(左或右)的图像上使用时的性能更高,而不是两者的组合。这意味着需要在数据进入分类模型之前对其进行区分。本文提出了一种基于cnn的左右向图像区分模型。对多个cnn进行了训练和测试,其中表现最好的是VGG16架构,准确率为0.99 (+/- 0.01),AUC为0.98(+/- 0.01)。这些结果表明,cnn可以通过在分类模型中使用之前分割数据来解决方向敏感性问题。
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