jonathanfeenstra / tuberculosis-elm-classifier

ELM classifier for detecting tuberculosis in lung X-rays

Extreme Learning Machine classifier for detecting tuberculosis in posterior-anterior chest radiographs

Data Mining project for the Radboud University (graded 9/10). See the notebook for our code and results.

Abstract

In this project we build an Extreme Learning Machine (ELM) capable of detecting TB in lung X-rays to support computer-aided diagnosis. The advantages of ELM include good generalization performance and fast learning speed, outperforming SVM in many classification tasks (Huang, Zhu & Siew, 2006). The classifier was made in Python using the HP-ELM library (Akusok, Bjork, Miche, & Lendasse, 2015). The performance was evaluated using ROC curves, confusion matrices and stratified ten-fold cross-validation. The accuracy, recall and F-measure vary between 0.81 and 0.85, the AUC varies between 0.87 and 0.90 for both the Montgomery- and Shenzen set, which is lower than state-of-the-art classifiers (0.962 and 0.991 respectively) (Rajaraman et al., 2018).

Datasets

Two datasets were used, the Montgomery County X-ray set and the Shenzen Hospital X-ray Set. The Montgomery County X-ray Set consists of 138 chest X-rays, of which 80 normal cases and 58 cases with manifestations of TB. The Shenzen Hospital Set consists 662 X-rays, of which 326 normal cases and 336 cases with manifestations of TB. A detailed description of the two datasets is provided by Jaeger et al. (2014), doi: 10.3978/j.issn.2223-4292.2014.11.20.

The datasets can be downloaded from: https://ceb.nlm.nih.gov/repositories/tuberculosis-chest-x-ray-image-data-sets/.

Built With

• Jupyter Notebook

Authors

• Jonathan Feenstra - @JonathanFeenstra
• Justin Huberts - @komjum6

Acknowledgements

We would like to thank @hmchuong for his implementation of bone suppression.

References

• Akusok, A., Bjork, K.-M., Miche, Y., & Lendasse, A. (2015). High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications. IEEE Access, 3, 1011-1025.
• Huang G.-B., Zhu Q.-Y. & Siew C.-K. (2006). Extreme Learning Machine: Theory and Applications. Neurocomputing 70, 489-501.
• Jaeger, S., Candemir, S., Antani, S., Wáng, Y., Lu, P., & Thoma, G. (2014). Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quantitative Imaging In Medicine And Surgery, 4(6), 475-477.
• Rajaraman, S., Candemir, S., Xue, Z., Alderson, P., Kohli, M., Abuya, J., . . . Antani, S. (2018). A novel stacked generalization of models for improved TB detection in chest radiographs. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 718-721). IEEE.