Keywords:
Metastases, Atelectasis, Screening, Computer Applications-Detection, diagnosis, Neural networks, Conventional radiography, CAD, Thorax, Lung, Cardiac
Authors:
N. Ramanauskas, J. Dementaviciene, J. Bialopetravičius, D. Barušauskas, J. Armaitis, J. Stankeviciene, G. Danys, R. Puronaite, R. Kizlaitis; Vilnius/LT
DOI:
10.1594/ecr2018/C-1896
Methods and materials
A database of CXR images (n = 116132) from 5 medical centers from 3 continents was used (listed in terms of quantity): Chest-xray8 [3],
Vilnius University hospital Santaros clinics,
openi [4],
tuberculosis_by [5],
tuberculosis_India [6],
tuberculosis_Montgomery [7],
tuberculosis_Shenzhen [8] .
To our knowledge,
this is the biggest CXR database reported in the context of a CXR CAD to date.
We used labels that were either provided with the images (Chest-xray8,
openi,
tuberculosis_by,
tuberculosis_India,
tuberculosis_Montgomery,
tuberculosis_Shenzhen) or text mined from the associated radiologist descriptions (Vilnius University hospital Santaros clinics dataset).
No additional relabelling was performed.
The data was divided into a training set (70%),
a testing set (20%),
and a validation set (10%).
A single convolutional neural network (CNN) based on ResNet was used to construct a model for CXR pathology classification.