Abstract
Tuberculosis (TB) is curable, and millions of deaths could be averted if diagnosed early. One of the sources of screening TB is through a chest X-ray. Still, its success depends on the interpretation of skilled and experienced radiologists, mostly lacking in high TB burden regions. However, with the intervention of a computer-aided detection system, TB can be automatically detected from chest X-rays. This paper presents an Ensemble model based on multiple pre-trained models to detect TB from chest X-rays automatically. The models were trained on the Shenzhen dataset and validated on the Montgomery dataset to achieve good generalization on a new (unseen) dataset. Improved classification accuracy was however achieved through the Ensemble model compared to the individual models. The proposed model indicates the strength of combining multiple models to improve model accuracy.
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References
Ahsan, M., Gomes, R., Denton, A.: Application of a convolutional neural network using transfer learning for tuberculosis detection. In: 2019 IEEE International Conference on Electro Information Technology (EIT), pp. 427–433. IEEE (2019)
Ayaz, M., Shaukat, F., Raja, G.: Ensemble learning based automatic detection of tuberculosis in chest x-ray images using hybrid feature descriptors. Phys. Eng. Sci. Med. 44, 1–12 (2021)
Bloice, M.D., Stocker, C., Holzinger, A.: Augmentor: an image augmentation library for machine learning. arXiv preprint arXiv:1708.04680 (2017)
Cicero, M., et al.: Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs. Invest. Radiol. 52(5), 281–287 (2017)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Hammen, I.: Tuberculosis mimicking lung cancer. Respir. Med. Case Rep. 16, 45–47 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Heo, S.J., et al.: Deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers’ health examination data. Int. J. Environ. Res. Public Health 16(2), 250 (2019)
Hernández, A., Panizo, Á., Camacho, D.: An ensemble algorithm based on deep learning for tuberculosis classification. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A.J., Menezes, R., Allmendinger, R. (eds.) IDEAL 2019. LNCS, vol. 11871, pp. 145–154. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33607-3_17
Hijazi, M.H.A., Hwa, S.K.T., Bade, A., Yaakob, R., Jeffree, M.S.: Ensemble deep learning for tuberculosis detection using chest x-ray and canny edge detected images. IAES Int. J. Artif. Intell. 8(4), 429 (2019)
Hooda, R., Mittal, A., Sofat, S.: Tuberculosis detection from chest radiographs: a comprehensive survey on computer-aided diagnosis techniques. Curr. Med. Imaging 14(4), 506–520 (2018)
Hwang, S., Kim, H.E., Jeong, J., Kim, H.J.: A novel approach for tuberculosis screening based on deep convolutional neural networks. In: Medical imaging 2016: Computer-Aided Diagnosis, vol. 9785, p. 97852W. International Society for Optics and Photonics (2016)
Jaeger, S., Candemir, S., Antani, S., Wáng, Y.X.J., Lu, P.X., Thoma, G.: Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg, 4(6), 475 (2014)
Karnkawinpong, T., Limpiyakorn, Y.: Chest x-ray analysis of tuberculosis by convolutional neural networks with affine transforms. In: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, pp. 90–93 (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Kurt, B., Nabiyev, V.V., Turhan, K.: Medical images enhancement by using anisotropic filter and clahe. In: 2012 International Symposium on Innovations in Intelligent Systems and Applications, pp. 1–4. IEEE (2012)
Lopes, U., Valiati, J.F.: Pre-trained convolutional neural networks as feature extractors for tuberculosis detection. Comput. Biol. Med. 89, 135–143 (2017)
Meraj, S.S., Yaakob, R., Azman, A., Rum, S.N.M., Nazri, A.: Artificial intelligence in diagnosing tuberculosis: a review. Int. J. Adv. Sci. Eng. Inf. Technol. 9(1), 81–91 (2019)
Oloko-Oba, M., Viriri, S.: Diagnosing tuberculosis using deep convolutional neural network. In: El Moataz, A., Mammass, D., Mansouri, A., Nouboud, F. (eds.) ICISP 2020. LNCS, vol. 12119, pp. 151–161. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51935-3_16
Oloko-Oba, M., Viriri, S.: Pre-trained convolutional neural network for the diagnosis of tuberculosis. In: Bebis, G., et al. (eds.) ISVC 2020. LNCS, vol. 12510, pp. 558–569. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64559-5_44
Oloko-Oba, M., Viriri, S.: Tuberculosis abnormality detection in chest x-rays: a deep learning approach. In: Chmielewski, L.J., Kozera, R., Orłowski, A. (eds.) ICCVG 2020. LNCS, vol. 12334, pp. 121–132. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59006-2_11
Organization, W.H., et al.: Chest radiography in tuberculosis detection: summary of current who recommendations and guidance on programmatic approaches. World Health Organization, Technical report (2016)
Organization, W.H., et al.: Global tuberculosis report 2019: executive summary (2019)
Organization, W.H., et al.: Global tuberculosis report 2020: executive summary (2020)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Parsons, L.M., et al.: Laboratory diagnosis of tuberculosis in resource-poor countries: challenges and opportunities. Clin. Microbiol. Rev. 24(2), 314–350 (2011)
Pasa, F., Golkov, V., Pfeiffer, F., Cremers, D., Pfeiffer, D.: Efficient deep network architectures for fast chest x-ray tuberculosis screening and visualization. Sci. Rep. 9(1), 1–9 (2019)
Rohilla, A., Hooda, R., Mittal, A.: Tb detection in chest radiograph using deep learning architecture. In: ICETETSM-17, pp. 136–147 (2017)
Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. arXiv preprint arXiv:1710.09829 (2017)
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Van’t Hoog, A., et al.: High sensitivity of chest radiograph reading by clinical officers in a tuberculosis prevalence survey. Int. J. Tuberculosis Lung Dis. 15(10), 1308–1314 (2011)
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Oloko-Oba, M., Viriri, S. (2021). Ensemble of Convolution Neural Networks for Automatic Tuberculosis Classification. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_41
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