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

arXiv: https://arxiv.org/abs/2009.05951 · HTML: https://mlsanity.com/paper/2009.05951/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2020.

## Built on by (most cited first, 1 of 1)

- [Image Projective Transformation Rectification with Synthetic Data for Smartphone-captured Chest X-ray Photos Classification](https://mlsanity.com/paper/2210.05954/) (2022)

## Abstract (arXiv)

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.