| Autor: |
Griffin, Terence, Chen, Qilei, Sun, Xinzi, Wang, Dechun, Brunette, Maria J., Cao, Yu, Liu, Benyuan |
| Předmět: |
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| Zdroj: |
International Journal of Semantic Computing; Mar2022, Vol. 16 Issue 1, p69-92, 24p |
| Abstrakt: |
Tuberculosis (TB) is a contagious disease affecting millions of people annually worldwide. Treatment of this disease and reduction in local epidemics can be improved markedly by increasing the speed and efficiency of screening and diagnosis. eRxNet is a pipeline of convolutional neural networks designed to provide healthcare professionals with detailed and accurate analysis of chest X-rays (CXRs) for TB screening. The pipeline combines whole image classification, object detection (bounding boxes), and instance segmentation (polygonal masks) to provide data analysis at varying levels of detail. In order to construct a high performing system, a comparison of different CNN architectures applied to these tasks is presented. Images from two large TB datasets, UML-Peru and TBX11K, were used for training and evaluation of the models. Combining the two datasets required the development of a preprocessing stage which includes lung segmentation and image enhancement. We show that the resulting four-stage pipeline of CNNs, using a combination of DenseNet, Faster R-CNN, and Mask R-CNN, has sufficiently strong performance to be a useful tool for TB screening. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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