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

TL;DR

This paper introduces a novel deep learning system trained on a new dataset to interpret smartphone-captured chest radiographs, achieving promising diagnostic accuracy and being the first to address this specific challenge.

Contribution

It presents the first deep learning-based approach specifically designed for smartphone-captured radiographs using the CheXphoto dataset.

Findings

Achieved 0.684 AUC in diagnosis accuracy

Reached 0.699 average F1 score

First study to process smartphone-captured radiographs

Abstract

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.