Convolutional neural networks with approximation of Shapley values for the classification and interpretation of pneumonia in X-ray images

  • Arthur Gabriel Mathias Marques PUC Minas
  • Alexei Manso Correa Machado PUC Minas / UFMG


Pneumonia is a lung disease responsible for the highest number of deaths from infection in children and adults. Its diagnosis must be fast and accurate so that procedures are taken as soon as possible to combat the disease. In this work, Convolutional Neural Networks were explored for the classification of chest radiography images in the context of pneumonia diagnosis. Although these models are highly effective, their predictions are difficult to interpret. Therefore, the proposed method additionally aims at presenting an explainable model based on Shapley approximation values to perform the diagnosis of pneumonia with higher robustness. Results show that the model achieves competitive accuracy when compared to other architectures, and overcome them with respect to interpretation abilities.


C. Chen, O. Li, D. Tao, A. Barnett, C. Rudin, and J. K. Su. This looks like that: deep learning for interpretable image recognition. Advances in neural information processing systems, 32, 2019.

A. Giełczyk, A. Marciniak, M. Tarczewska, and Z. Lutowski. Pre-processing methods in chest x-ray image classification. Plos one, 17(4):e0265949, 2022.

D. Kermany, K. Zhang, M. Goldbaum, et al. Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley data, 2(2), 2018.

S. M. Lundberg and S.-I. Lee. A unified approach to interpreting model predictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc., 2017.

H. Panwar, P. Gupta, M. K. Siddiqui, R. Morales-Menendez, P. Bhardwaj, and V. Singh. A deep learning and grad-cam based color visualization approach for fast detection of covid-19 cases using chest x-ray and ct-scan images. Chaos, Solitons & Fractals, 140: 110190, 2020.

S. Pereira, R. Meier, V. Alves, M. Reyes, and C. A. Silva. Automatic brain tumor grading from mri data using convolutional neural networks and quality assessment. In Understanding and interpreting machine learning in medical image computing applications, pages 106–114. Springer, 2018.

S. Ravi, S. Khoshrou, and M. Pechenizkiy. Vidi: Descriptive visual data clustering as radiologist assistant in covid-19 streamline diagnostic. arXiv preprint arXiv:2011.14871, 2020.

H. Saleem, A. R. Shahid, and B. Raza. Visual interpretability in 3d brain tumor segmentation network. Computers in Biology and Medicine, 133:104410, 2021.

A. Shrikumar, P. Greenside, and A. Kundaje. Learning important features through propagating activation differences. In International conference on machine learning, pages 3145–3153. PMLR, 2017.

G. Singh and K.-C. Yow. An interpretable deep learning model for covid-19 detection with chest x-ray images. Ieee Access, 9:85198–85208, 2021.

S. Singla, B. Pollack, S. Wallace, and K. Batmanghelich. Explaining the black-box smoothly-a counterfactual approach. arXiv preprint arXiv:2101.04230, 2021.

UNICEF. A child dies of pneumonia every 43 seconds, 2022. [link], Last accessed on 202210-30.
MARQUES, Arthur Gabriel Mathias; MACHADO, Alexei Manso Correa. Convolutional neural networks with approximation of Shapley values for the classification and interpretation of pneumonia in X-ray images. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 234-243. ISSN 2763-8952. DOI: