Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs
This addresses the need for faster and more transparent diagnosis of multiple thoracic diseases in medical imaging, though it is incremental as it applies an existing DenseNet method to a multi-label task.
The paper tackled the problem of automated multi-label disease diagnosis from chest X-rays, achieving an AUC of 0.896 for Cardiomegaly and 0.655 for Nodule with corresponding accuracies.
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. Still, the performance of such systems is opaque to end-users and limited to detecting a single pathology. In this paper, we propose a multi-label disease prediction model that allows the detection of more than one pathology at a given test time. We use a dense convolutional neural network (DenseNet) for disease diagnosis. 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 build trust in decision-making, we generated heatmaps on X-rays to visualize the regions where the model paid attention to make certain predictions. Our proposed automated disease prediction model obtained highly confident high-performance metrics in multi-label disease prediction tasks.