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

An XAI-Based Deep Learning Framework for Coronary Artery Disease Diagnosis using SPECT MPI polar map images

, , , , ,

DOI: http://dx.doi.org/10.15439/2022R06

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 235241 ()

Full text

Abstract. Our study aimed to develop an explanatory method for predicting Coronary Artery Disease (CAD) classification using spect images. As we all know, deep neural networks usually consist of many layers connected to each other through interlocking network nodes. Even if we check the classes and describe their relationships, it is difficult to understand entirely how active neural networks make predictions. Therefore, deep learning is still considered a``Black box''. Existing XAI (eXplainable Artificial Intelligence) approach can provide insights into the inside of a Deep Learning model allowing for transparency and interpretation. Our previous research helps doctors diagnose the CAD of patients by developing deep learning models using a multi-stage transfer learning framework. The model achieved 0.955 accuracy, 0.932 AUC, 0.944 sensitivity, and 0.889 specificity, showing effective performance. Our dataset includes 218 SPECT images from 218 imported patients collected at 108 Hospital in Hanoi, Vietnam. In this paper, We propose an explainable Deep Learning framework using three popular XAI approaches: LIME, GradCam, and RISE. These XAI approaches are effective tools for interpreting the prediction of deep learning models. We evaluate the effectiveness of the interpretation by visualizing the explained regions and using improved deletion and insertion with a threshold limit suitable for Binary Classification. The experiment results show that our model effectively diagnoses CAD and provides medical interpretation. Furthermore, the proposed method for evaluating the deletion and insertion metrics is considered more efficient for binary classification than the traditional metrics


  1. Erito Marques de Souza Filho, Fernando de Amorim Fernandes, Christiane Wiefels, Lucas Nunes Dalbonio de Carvalho et al., “Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps,” 2021 Nov.
  2. Nikolaos I Papandrianos, Anna Feleki, Elpiniki I Papageorgiou, Chiara Martini, “Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images,” 2022 July.
  3. P. N. Hai, N. C. Thanh, N. T. Trung, T. T. Kien, “Transfer Learning for Disease Diagnosis from Myocardial Perfusion SPECT Imaging,” Computers, Materials and Continua, Vol.3. pp. 5925-5941, 2022 July.
  4. Fagan, L. M.; Shortliffe, E. H.; Buchanan, B. G. (1980), “Computerbased medical decision making: from MYCIN to VM. Automedica,” Heuristic Programming Project, Departments of Medicine and Computer Science Stanford University, Stanford, California.
  5. Alizadeh, Fatemeh (2021). "I Don't Know, Is AI Also Used in Airbags?: An Empirical Study of Folk Concepts and People's Expectations of Current and Future Artificial Intelligence". Icom. 20 (1): 3–17.
  6. Brown, John S.; Burton, R. R.; De Kleer, Johan, “Pedagogical, natural language, and knowledge engineering techniques,” SOPHIE I, II, and II. Intelligent Tutoring Systems. Academic Press, Vol. 4, pp. 98-111, 2016.
  7. Bareiss, Ray; Porter, Bruce; Weir, Craig; Holte, Robert, Protos, “An Exemplar-Based Learning Apprentice. Machine Learning,” Morgan Kaufmann Publishers Inc, Vol. 3, pp. 112–139, 2019.
  8. Bareiss, Ray, “Exemplar-Based Knowledge Acquisition: A Unified Approach to Concept Representation, Classification, and Learning. Perspectives in Artificial Intelligence,” 2001.
  9. Tickle, A. B.; Andrews, R.; Golea, M.; Diederich, J. ,”The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural network,” IEEE Transactions on Neural Networks, Vol.5. , No. 10-12, pp 1057-1068, 2018 Nov.
  10. Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin, “Why Should I Trust You?” Explaining the Predictions of Any Classifier,” Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, Vol.2., No.1-3, pp 97-101, 2016 June.
  11. Scott M. Lundberg, SuIn Lee: “A Unified Approach to Interpreting Model Predictions,” School of Computer Science University of Washington Seattle, WA 98105, 2017.
  12. Pang Wei Koh, Percy Liang, “Understanding Black-box Predictions via Influence Functions,” 2017.
  13. Mukund Sundararajan, Ankur Taly, Qiqi Yan, “Axiomatic Attribution for Deep Networks,” 2017.
  14. Vitali Petsiuk, Abir Das, Kate Saenko, “RISE: Randomized Input Sampling for Explanation of Black-box Models,” 2018.
  15. Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradientbased Localization,” 2019.
  16. N. I. Papandrianos, A. Feleki, S. Moustakidis, E. I. Papageorgiou Ioannis, D. Apostolopoulos, D. J. Apostolopoulos, “An Explainable Classification Method of SPECT Myocardial Perfusion Images in Nuclear Cardiology Using Deep Learning and Grad-CAM,” 2021.
  17. Liu, H.; Wu, J.; Miller, “Diagnostic Accuracy of Stress-Only Myocardial Perfusion SPECT Improved by Deep Learning,” Eur. J. Nucl. Med. Mol. Imaging, Vol.48., pp. 2793–2800, 2021.
  18. Otaki, Y.; Singh, A.; Kavanagh, P.; Miller, R.J.H.; Parekh, T.; Tamarappoo, B.K.; Sharir, T.; Einstein, A.J.; Fish, M.B.; Ruddy, T.D, “Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease,” JACC Cardiovasc. Imaging 2021, Vol.4. , No.3-5. , pp. 99-110, 2019
  19. Ruth C Fong and Andrea Vedaldi, “Interpretable Explanations of Black Boxes by Meaningful Perturbation,” In IEEE International Conference on Computer Vision 2017 Oct, Vol.3., pp.115-121, 2017.
  20. Laura Ruis, Mitchell Stern, Julia Proskurnia, William Cha: “Insertion-Deletion Transformer,” 2020 Jan.