Chest X-ray images are one of the most common medical diagnosis techniques to identify different thoracic diseases. However, identification of pathologies in X-ray images requires skilled manpower and are often cited as a time-consuming task with varied level of interpretation, particularly in cases where the identification of disease only by images is difficult for human eyes. With recent achievements of deep learning in image classification, its application in disease diagnosis has been widely explored. This research project presents a multi-label disease diagnosis model of chest x-rays. Using Dense Convolutional Neural Network (DenseNet), the diagnosis system was able to obtain high classification predictions. The model obtained the highest AUC score of 0.896 for condition Cardiomegaly and the lowest AUC score for Nodule, 0.655. The model also localized the parts of the chest radiograph that indicated the presence of each pathology using GRADCAM, thus contributing to the model interpretability of a deep learning algorithm.

翻译：切斯特X射线图象是发现不同胸腔疾病最常见的医学诊断技术之一,然而,在X射线图象中查明病理需要熟练的人力,常常被说成是一项耗时的工作,需要不同程度的判读,特别是当只有图像才能辨别疾病对人类眼睛来说很困难时。由于最近在图像分类方面的深层学习,在疾病诊断中的应用得到了广泛探讨。这个研究项目提供了胸部X射线多标签疾病诊断模型。利用Dense Convolution Neal网络(DenseNet),诊断系统能够获得高分类预测。该模型获得了心血管病症0.896的ACU最高分和结核病症AUC最低分0.655。该模型还把显示使用GRADCAM的每一种病理学的胸部射线图部分本地化,从而有助于深层次学习算法的模型的可解释性。