Traditional methods of identifying pathologies in X-ray images rely heavily on skilled human interpretation and are often time-consuming. The advent of deep learning techniques has enabled the development of automated disease diagnosis systems, but the performance of such systems is dependent on the quality of the model and the level of interpretability it provides. In this paper, we propose a multi-label disease diagnosis model for chest X-rays using a dense convolutional neural network (DenseNet) and model interpretability using GRADCAM. We trained our model using frontal X-rays and evaluated its performance using various quantitative metrics, including the area under the receiver operating characteristic curve (AUC). Our proposed model achieved the highest AUC score of 0.896 for the condition Cardiomegaly with an accuracy of 0.826, while the lowest AUC score was obtained for Nodule, at 0.655 with an accuracy of 0.66. To promote model interpretability and build trust in decision making, we generated heatmaps on X-rays to visualize the regions where the model paid attention to make certain predictions. Additionally, we estimated the uncertainty in model predictions by presenting the confidence interval of our measurements. Our proposed automated disease diagnosis model obtained high performance metrics in multi-label disease diagnosis tasks and provided visualization of model predictions for model interpretability.

翻译：传统X光片病理识别方法高度依赖专业人工判读且耗时较长。深度学习技术的发展推动了自动化疾病诊断系统的开发，但这类系统的性能取决于模型质量及其可解释性水平。本文提出了一种基于密集卷积神经网络（DenseNet）的胸部X光片多标签疾病诊断模型，并利用GRADCAM实现模型可解释性。我们采用正面X光片训练模型，通过包括受试者工作特征曲线下面积（AUC）在内的多种定量指标评估其性能。所提模型对心脏肥大诊断取得最高AUC值0.896（准确率0.826），对结节诊断取得最低AUC值0.655（准确率0.66）。为提升模型可解释性并建立决策信任度，我们生成X光片热力图以可视化模型做出特定预测时关注的区域。此外，通过呈现测量的置信区间估算模型预测的不确定性。本文提出的自动化疾病诊断模型在多标签疾病诊断任务中取得了高性能指标，并通过预测结果可视化提升了模型可解释性。