CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

11/14/2017
by   Pranav Rajpurkar, et al.
0

We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on pneumonia detection on both sensitivity and specificity. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases.

READ FULL TEXT

page 1

page 5

05/19/2020

Deep learning classification of chest x-ray images

We propose a deep learning based method for classification of commonly o...
05/13/2020

Context Learning for Bone Shadow Exclusion in CheXNet Accuracy Improvement

Chest X-ray examination plays an important role in lung disease detectio...
04/20/2018

Large Scale Automated Reading of Frontal and Lateral Chest X-Rays using Dual Convolutional Neural Networks

The MIMIC-CXR dataset is (to date) the largest publicly released chest x...
03/08/2021

CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays

We systematically evaluate the performance of deep learning models in th...
12/21/2018

Detection of distal radius fractures trained by a small set of X-ray images and Faster R-CNN

Distal radius fractures are the most common fractures of the upper extre...
01/23/2017

Learning what to look in chest X-rays with a recurrent visual attention model

X-rays are commonly performed imaging tests that use small amounts of ra...

Please sign up or login with your details

Forgot password? Click here to reset