Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs
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...
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| Main Authors | , |
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| Format | Journal Article |
| Language | English |
| Published |
07.02.2022
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| Subjects | |
| Online Access | Get full text |
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| Summary: | 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. |
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| DOI: | 10.48550/arxiv.2202.03583 |