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Recently, computer-aided diagnostic systems (CADs) that could automaticallyinterpret medical images effectively have been the emerging subject of recentacademic attention. For radiographs, several deep learning-based systems ormodels have been developed to study the multi-label diseases recognition tasks.However, none of them have been trained to work on smartphone-captured chestradiographs. In this study, we proposed a system that comprises a sequence ofdeep learning-based neural networks trained on the newly released CheXphotodataset to tackle this issue. The proposed approach achieved promising resultsof 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 processingsmartphone-captured radiographs.

Interpretation of smartphone-captured radiographs utilizing a deep learning-based approach