A robust network architecture to detect normal chest X-ray radiographs
We propose a novel deep neural network architecture for normalcy detection in chest x-ray
images. This architecture treats the problem as fine-grained binary classification in which the …
images. This architecture treats the problem as fine-grained binary classification in which the …
Can artificial intelligence reliably report chest x-rays?: Radiologist validation of an algorithm trained on 2.3 million x-rays
P Putha, M Tadepalli, B Reddy, T Raj… - arXiv preprint arXiv …, 2018 - arxiv.org
Background: Chest X-rays are the most commonly performed, cost-effective diagnostic
imaging tests ordered by physicians. A clinically validated AI system that can reliably …
imaging tests ordered by physicians. A clinically validated AI system that can reliably …
Identifying disease-free chest x-ray images with deep transfer learning
Chest X-rays (CXRs) are among the most commonly used medical image modalities. They
are mostly used for screening, and an indication of disease typically results in subsequent …
are mostly used for screening, and an indication of disease typically results in subsequent …
Local adaptation improves accuracy of deep learning model for automated x-ray thoracic disease detection: A thai study
I Chamveha, T Tongdee, P Saiviroonporn… - arXiv preprint arXiv …, 2020 - arxiv.org
Despite much promising research in the area of artificial intelligence for medical image
diagnosis, there has been no large-scale validation study done in Thailand to confirm the …
diagnosis, there has been no large-scale validation study done in Thailand to confirm the …
Generalizable inter-institutional classification of abnormal chest radiographs using efficient convolutional neural networks
Our objective is to evaluate the effectiveness of efficient convolutional neural networks
(CNNs) for abnormality detection in chest radiographs and investigate the generalizability of …
(CNNs) for abnormality detection in chest radiographs and investigate the generalizability of …
End-to-end deep diagnosis of x-ray images
K Urinbayev, Y Orazbek, Y Nurambek… - 2020 42nd Annual …, 2020 - ieeexplore.ieee.org
We present an end-to-end deep learning frame-work for X-ray image diagnosis. As the first
step, our system determines whether a submitted image is an X-ray or not. After it classifies …
step, our system determines whether a submitted image is an X-ray or not. After it classifies …
Detection of pathologies in X-Ray chest images using a deep convolutional neural network with appropriate data augmentation techniques
Current advances in trained Deep Learning models have allowed architectures, such as
Convolutional Neural Networks to outperformed radiologists in developing complex tasks …
Convolutional Neural Networks to outperformed radiologists in developing complex tasks …
[HTML][HTML] Using artificial intelligence to stratify normal versus abnormal chest X-rays: external validation of a deep learning algorithm at East Kent Hospitals University …
Background: The chest radiograph (CXR) is the most frequently performed radiological
examination worldwide. The increasing volume of CXRs performed in hospitals causes …
examination worldwide. The increasing volume of CXRs performed in hospitals causes …
[HTML][HTML] Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study
YD Cid, M Macpherson, L Gervais-Andre… - The Lancet Digital …, 2024 - thelancet.com
Background Artificial intelligence (AI) systems for automated chest x-ray interpretation hold
promise for standardising reporting and reducing delays in health systems with shortages of …
promise for standardising reporting and reducing delays in health systems with shortages of …
Abnormality detection and localization in chest x-rays using deep convolutional neural networks
Chest X-Rays (CXRs) are widely used for diagnosing abnormalities in the heart and lung
area. Automatically detecting these abnormalities with high accuracy could greatly enhance …
area. Automatically detecting these abnormalities with high accuracy could greatly enhance …