# Computer-Aided Diagnosis of Pulmonary Fibrosis Using Deep Learning and CT Images

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/computer-aided-diagnosis-of-pulmonary-fibrosis-using-deep-learning-and-ct-images/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Andreas Christe,Alan A. Peters,Dionysios Drakopoulos,Johannes T. Heverhagen,Thomas Geiser,Thomai Stathopoulou,Stergios Christodoulidis,Marios Anthimopoulos,Stavroula Mougiakakou,Lukas Ebner |
| Citations | 145 |
| DOI | 10.1097/rli.0000000000000574 |
| Fields | Medicine |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://doi.org/10.1097/rli.0000000000000574 |
| OpenAlex ID | https://openalex.org/W2944540533 |
| PMID | 31483764 |
| Type | article |
| Year | 2019 |

## Paper authors

- [Stavroula Mougiakakou](https://scholariq.org/researchers/stavroula-mougiakakou/)

## Paper primary topic

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)

## Paper topics

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)
- [Lung Cancer Diagnosis and Treatment](https://scholariq.org/topics/lung-cancer-diagnosis-and-treatment/)
- [Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis](https://scholariq.org/topics/interstitial-lung-diseases-and-idiopathic-pulmonary-fibrosis/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.