Logo PTI Logo rice

Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

A Deep Learning Approach with Stack of Sub-classifiers for Multi-label Classification of Obstructive Disease from Myocardial Perfusion SPECT

, , ,

DOI: http://dx.doi.org/10.15439/2022R07

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 261–266 ()

Full text

Abstract. Artificial intelligence applications, especially deep learning in medical imaging, have gained much attention in recent years. With the computer's aid, Coronary artery disease (CAD) - one of the most dangerous cardiovascular diseases - is diagnosed effectively without human interference and efforts. A lot of research involving predicting CAD from Myocardial Perfusion SPECT has been conducted and given impressive results. However, all existing methods detect whether there is a disease or not. They do not provide information about which obstructive areas are (mainly in the left anterior descending artery (LAD), left circumflex artery (LCx), and right coronary artery (RCA) territories) that result in CAD. To further diagnose CAD, we develop new classifiers to solve a multi-label classification problem with the highest accuracy and area under the receiver operating characteristics curve (AUC) when compared to different methods. Our proposed method is based on transfer learning to extract features from Myocardial Perfusion SPECT Polar Maps and a novel stack of sub-classifiers to detect particularly obstructive areas. We evaluated our methods with eight hundred and one obstructive images from a database of patients referred to a hospital from 2017 to 2019

References

  1. Cardiovascular diseases. World Health Organization, https://www.who.int/health-topics/cardiovascular-diseases, accessed on 2022-06-14
  2. Coronary artery disease. Mayo Foundation for Medical Education and Research (May 2022), https://www.mayoclinic.org/diseases-conditions/coronary-artery-disease/symptoms-causes/syc-20350613, accessed on 2022-06-14
  3. Apostolopoulos, I., Papathanasiou, N., Spyridonidis, T., Apostolopoulos, D.: Automatic characterization of myocardial perfusion imaging polar maps employing deep learning and data augmentation. Hellenic journal of nuclear medicine 23 (07 2020). https://doi.org/10.1967/s002449912101
  4. Betancur, J., Commandeur, F., Motlagh, M., Sharir, T., Einstein, A.J., Bokhari, S., Fish, M.B., Ruddy, T.D., Kaufmann, P., Sinusas, A.J., et al.: Deep learning for prediction of obstructive disease from fast myocardial perfusion spect: a multicenter study. JACC: Cardiovascular Imaging 11(11), 1654–1663 (2018)
  5. Betancur, J., Hu, L.H., Commandeur, F., Sharir, T., Einstein, A.J., Fish, M.B., Ruddy, T.D., Kaufmann, P.A., Sinusas, A.J., Miller, E.J., et al.: Deep learning analysis of upright-supine high-efficiency spect myocardial perfusion imaging for prediction of obstructive coronary artery disease: a multicenter study. Journal of Nuclear Medicine 60(5), 664–670 (2019)
  6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. pp. 770–778 (06 2016). https://doi.org/10.1109/CVPR.2016.90
  7. Hesse, B., Tägil, K., Cuocolo, A., Anagnostopoulos, C., Bardiès, M., Bax, J., Bengel, F., Busemann Sokole, E., Davies, G., Dondi, M., et al.: Eanm/esc procedural guidelines for myocardial perfusion imaging in nuclear cardiology. European journal of nuclear medicine and molecular imaging 32(7), 855–897 (2005)
  8. Holly, T., Abbott, B., Al-Mallah, M., Calnon, D., Cohen, M., DiFilippo, F., Ficaro, E., Freeman, M., Hendel, R., Jain, D., Leonard, S., Nichols, K., Polk, D., Soman, P.: Single photon-emission computed tomography (10 2010). https://doi.org/10.1007/s12350-010-9246-y
  9. Kaplan Berkaya, S., Ak, I., Gunal, S.: Classification models for spect myocardial perfusion imaging. Computers in Biology and Medicine 123, 103893 (07 2020). https://doi.org/10.1016/j.compbiomed.2020.103893
  10. Papandrianos, N., Papageorgiou, E.: Automatic diagnosis of coronary artery disease in spect myocardial perfusion imaging employing deep learning. Applied Sciences 11(14), 6362 (2021)
  11. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556 (09 2014)
  12. de Souza Filho, E.M., Fernandes, F.d.A., Wiefels, C., de Carvalho, L.N.D., dos Santos, T.F., dos Santos, A.A.S.M.D., Mesquita, E.T., Seixas, F.L., Chow, B.J.W., Mesquita, C.T., Gismondi, R.A.: Machine learning algorithms to distinguish myocardial perfusion spect polar maps. Frontiers in Cardiovascular Medicine 8, 1437 (2021). https://doi.org/10.3389/fcvm.2021.741667, https://www.frontiersin.org/article/10.3389/fcvm.2021.741667
  13. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. pp. 2818–2826 (06 2016). https://doi.org/10.1109/CVPR.2016.308