Article ; Online: Deep learning COVID-19 detection bias: accuracy through artificial intelligence.
2020 Volume 44, Issue 8, Page(s) 1539–1542
Abstract: Background: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported ... ...
| Abstract | Background: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests. Methods: We developed a deep learning model to improve accuracy of reported cases and to precisely predict the disease from chest X-ray scans. Our model relied on convolutional neural networks (CNNs) to detect structural abnormalities and disease categorization that were keys to uncovering hidden patterns. To do so, a transfer learning approach was deployed to perform detections from the chest anterior-posterior radiographs of patients. We used publicly available datasets to achieve this. Results: Our results offer very high accuracy (96.3%) and loss (0.151 binary cross-entropy) using the public dataset consisting of patients from different countries worldwide. As the confusion matrix indicates, our model is able to accurately identify true negatives (74) and true positives (32); this deep learning model identified three cases of false-positive and one false-negative finding from the healthy patient scans. Conclusions: Our COVID-19 detection model minimizes manual interaction dependent on radiologists as it automates identification of structural abnormalities in patient's CXRs, and our deep learning model is likely to detect true positives and true negatives and weed out false positive and false negatives with > 96.3% accuracy. |
|---|---|
| MeSH term(s) | Adolescent ; Adult ; Aged ; Aged, 80 and over ; Betacoronavirus ; Bias ; COVID-19 ; Child ; Coronavirus Infections ; Deep Learning ; Female ; Humans ; Male ; Middle Aged ; Neural Networks, Computer ; Pandemics ; Pneumonia, Viral ; SARS-CoV-2 ; Young Adult |
| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-05-27 |
| Publishing country | Germany |
| Document type | Journal Article |
| ZDB-ID | 80384-4 |
| ISSN | 1432-5195 ; 0341-2695 |
| ISSN (online) | 1432-5195 |
| ISSN | 0341-2695 |
| DOI | 10.1007/s00264-020-04609-7 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
Full text online
More links
Kategorien
In stock of ZB MED Cologne/Königswinter
| Zs.A 1324: Show issues | Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG) |
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.