CNN model for the automated detection of conditions on Chest X-rays.pdf (648.36 kB)
Download filePerformance of a deep learning CNN model for the automated detection of 13 common conditions on Chest X-rays
preprint
posted on 2021-10-06, 18:37 authored by Tirupathi Karthik, Vijayalakshmi KasiramanVijayalakshmi Kasiraman, Bhavani Paski, Kashyap Gurram, Amit Talwar, Monisha DonkenaBackground and aims: Chest X-rays are
widely used, non-invasive, cost effective imaging tests.
However, the complexity of interpretation and global
shortage of radiologists have led to reporting backlogs,
delayed diagnosis and a compromised quality of care. A
fully automated, reliable artificial intelligence system that
can quickly triage abnormal images for urgent radiologist
review would be invaluable in the clinical setting. The aim
was to develop and validate a deep learning Convoluted
Neural Network algorithm to automate the detection of 13
common abnormalities found on Chest X-rays.
Method: In this retrospective study, a VGG 16 deep
learning model was trained on images from the Chest-ray
14, a large publicly available Chest X-ray dataset,
containing over 112,120 images with annotations. Images
were split into training, validation and testing sets and
trained to identify 13 specific abnormalities. The primary
performance measures were accuracy and precision.
Results: The model demonstrated an overall accuracy of
88% in the identification of abnormal X-rays and 87% in the
detection of 13 common chest conditions with no model
bias.
Conclusion: This study demonstrates that a well-trained
deep learning algorithm can accurately identify multiple
abnormalities on X-ray images. As such models get further
refined, they can be used to ease radiology workflow
bottlenecks and improve reporting efficiency. Napier
Healthcare’s team that developed this model consists of
medical IT professionals who specialize in AI and its
practical application in acute & long-term care settings.
This is currently being piloted in a few hospitals and
diagnostic labs on a commercial basis.
Funding
Napier Healthcare Solutions Private Ltd
History
Email Address of Submitting Author
k.vijayalakshmi@napierhealthcare.comORCID of Submitting Author
0000-0002-2038-0202Submitting Author's Institution
Napier Healthcare Solutions Private LtdSubmitting Author's Country
- Singapore