Interpretation of smartphone-captured radiographs utilizing a deep learning-based approach
This work addresses a domain-specific problem for medical diagnostics by enabling automated analysis of smartphone-captured radiographs, but it is incremental as it applies existing deep learning methods to a new type of data.
The study tackled the problem of interpreting smartphone-captured chest radiographs, which had not been addressed by previous deep learning models, and achieved results of 0.684 in AUC and 0.699 in average F1 score using a system trained on the CheXphoto dataset.
Recently, computer-aided diagnostic systems (CADs) that could automatically interpret medical images effectively have been the emerging subject of recent academic attention. For radiographs, several deep learning-based systems or models have been developed to study the multi-label diseases recognition tasks. However, none of them have been trained to work on smartphone-captured chest radiographs. In this study, we proposed a system that comprises a sequence of deep learning-based neural networks trained on the newly released CheXphoto dataset to tackle this issue. The proposed approach achieved promising results of 0.684 in AUC and 0.699 in average F1 score. To the best of our knowledge, this is the first published study that showed to be capable of processing smartphone-captured radiographs.