Urinbayev Kudaibergen, Orazbek Yerassyl, Nurambek Yernur, Mirzakhmetov Almas, Varol Huseyin Atakan
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:2182-2185. doi: 10.1109/EMBC44109.2020.9175208.
We present an end-to-end deep learning frame-work for X-ray image diagnosis. As the first step, our system determines whether a submitted image is an X-ray or not. After it classifies the type of the X-ray, it runs the dedicated abnormality classification network. In this work, we only focus on the chest X-rays for abnormality classification. However, the system can be extended to other X-ray types easily. Our deep learning classifiers are based on DenseNet-121 architecture. The test set accuracy obtained for 'X-ray or Not', 'X-ray Type Classification', and 'Chest Abnormality Classification' tasks are 0.987, 0.976, and 0.947, respectively, resulting into an end-to-end accuracy of 0.91. For achieving better results than the state-of-the-art in the 'Chest Abnormality Classification', we utilize the new RAdam optimizer. We also use Gradient-weighted Class Activation Mapping for visual explanation of the results. Our results show the feasibility of a generalized online projectional radiography diagnosis system.
我们提出了一种用于X射线图像诊断的端到端深度学习框架。第一步，我们的系统确定提交的图像是否为X射线图像。在对X射线类型进行分类后，它会运行专门的异常分类网络。在这项工作中，我们仅专注于胸部X射线的异常分类。不过，该系统可以轻松扩展到其他X射线类型。我们的深度学习分类器基于DenseNet - 121架构。“是否为X射线”“X射线类型分类”和“胸部异常分类”任务的测试集准确率分别为0.987、0.976和0.947，最终端到端准确率为0.91。为了在“胸部异常分类”中取得比现有技术更好的结果，我们使用了新的RAdam优化器。我们还使用梯度加权类激活映射对结果进行可视化解释。我们的结果表明了通用在线投影放射摄影诊断系统的可行性。
