In recent years, with numerous developments of convolutional neural network (CNN) classification models for medical diagnosis, the issue of misrecognition/misclassification has become more and more important. Thus, research on misrecognition/misclassification has been progressing. This study focuses on the problem of misrecognition/misclassification of CNN classification models for coronavirus disease (COVID-19) using chest X-ray images. We construct two models for COVID-19 pneumonia classification by fine-tuning ResNet-50 architecture, i.e., a model retrained with full-sized original images and a model retrained with segmented images. The present study demonstrates the uncertainty (misrecognition/misclassification) of model performance caused by the discrepancy in the shapes of images at the phase of model construction and that of clinical applications. To achieve it, we apply three XAI methods to demonstrate and explain the uncertainty of classification results obtained from the two constructed models assuming for clinical applications. Experimental results indicate that the performance of classification models cannot be maintained when the type of constructed model and the geometric shape of input images are not matched, which may bring about misrecognition in clinical applications. We also notice that the effect of adversarial attack might be induced if the method of image segmentation is not performed properly. The results suggest that the best approach to obtaining a highly reliable prediction in the classification of COVID-19 pneumonia is to construct a model using full-sized original images as training data and use full-sized original images as the input when utilized in clinical applications.
References
[1]
Wang, L., Lin, Z.Q. and Wong, A. (2020) COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images. Scientific Reports, 10, Article No.19549. https://doi.org/10.1038/s41598-020-76550-z
[2]
Guefrechi, S., Jabra, M.B., Ammar, A., Koubaa, A. and Hamam, H. (2021) Deep Learning Based-Detection of COVID-19 from Chest X-Ray Images. Multimedia Tools and Applications, 80, 31803-31820. https://doi.org/10.1007/s11042-021-11192-5
[3]
Wu, C., Khishe, M., Mohammadi, M., Karim, S.H.T. and Rashid, T.A. (2021) Evolving Deep Convolutional Neutral Network by Hybrid Sine-Cosine and Extreme Learning Machine for Real-Time COVID19 Diagnosis from X-Ray Images. Soft Computing. https://doi.org/10.1007/s00500-021-05839-6
[4]
Karthik, R., Menaka, R. and Hariharan, M. (2020) Learning Distinctive Filters for COVID-19 Detection from Chest X-Ray Using Shuffled Residual CNN. Applied Soft Computing, 99, Article ID: 106744. https://doi.org/10.1016/j.asoc.2020.106744
[5]
Ribeiro, M.T., Singh, S. and Guestrin, C. (2016) “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13-17 August 2016, 1135-1144. https://doi.org/10.1145/2939672.2939778
[6]
Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J. and Oermann, E.K. (2018) Variable Generalization Performance of a Deep Learning Model to Detect Pneumonia in Chest Radiographs: A Cross-Sectional Study. PLOS Medicine, 6, e1002683. https://doi.org/10.1371/journal.pmed.1002683
[7]
Narodytska, N. and Kasiviswanathan, S.P. (2016) Simple Black-Box Adversarial Perturbations for Deep Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, 21-26 July 2017, 1310-1318. https://doi.org/10.1109/CVPRW.2017.172
[8]
Goodfellow, I.J., Shlens, J. and Szegedy, C. (2015) Explaining and Harnessing Adversarial Examples. arXiv:1412.6572v3.
[9]
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I. and Fergus, R. (2014) Intriguing Properties of Neural Networks. arXiv:1312.6199.
[10]
Kholiavchenko, M., Sirazitdinov, I., Kubrak, K., Badrutdinova, R., Kuleev, R., et al. (2020) Contour-Aware Multi-Label Chest X-ray Organ Segmentation. International Journal of Computer Assisted Radiology and Surgery, 15, 425-436.
[11]
Yu, P., Xu, H., Zhu, Y., Yang, C., Sun, X. and Zhao, J. (2011) An Automatic Computer-Aided Detection Scheme for Pneumoconiosis on Digital Chest Radiographs. Journal of Digital Imaging, 24, 382-393.
[12]
Salehi, S., Abedi, A., Balakrishnan, S. and Gholamrezanezhad, A. (2020) Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. American Journal of Roentgenology, 215, 87-93.
[13]
Narayanan, B.N., Silva, M.S.D., Hardie, R.C., Nathan K. Kueterman, N.K. and Ali, R. (2019) Understanding Deep Neural Network Predictions for Medical Imaging Applications. arXiv:1912.09621v1.
[14]
Narayanan, B.N., Davuluru, V.S.P. and Hardie, R.C. (2020) Two-Stage Deep Learning Architecture for Pneumonia Detection and Its Diagnosis in Chest Radiographs. Proceedings of SPIE Medical Imaging 2020, Houston, 2 March 2020, 113180G. https://doi.org/10.1117/12.2547635
[15]
He, K., Zhang, X., Ren, S. and Sun, J. (2016) Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 770-778. https://doi.org/10.1109/CVPR.2016.90
[16]
ImageNet. http://www.image-net.org
[17]
Samek, W., Wiegand, T. and Müller, K.R. (2017) Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models. arXiv:1708.08296. https://arxiv.org/abs/1708.08296
[18]
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Pedreschi, D. and Giannotti, F. (2018) A Survey of Methods For Explaining Black Box Models. ACM Computing Surveys, 51, Article No. 93. https://doi.org/10.1145/3236009
[19]
Rajaraman, S., Silamut, K., Hossain, A., Ersoy, I., Maude, R.J., Jaeger, S., et al. (2018) Understanding the Learned Behavior of Customized Convolutional Neural Networks toward Malaria Parasite Detection in Thin Blood Smear Images. Journal of Medical Imaging, 5, Article ID: 034501. https://doi.org/10.1117/1.JMI.5.3.034501
[20]
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D. and Batra, D. (2017) Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. 2017 IEEE International Conference on Computer Vision, Venice, 22-29 October 2017, 618-626. https://doi.org/10.1109/ICCV.2017.74
[21]
Sait, U., Gokul, L., Sunny, P., Rahul, B, Tarun, K., Sanjana S. and Kriti, B. (2021) Curated Dataset for COVID-19 Posterior-Anterior Chest Radiography Images (X-Rays). Mendeley Data.
[22]
http://imgcom.jsrt.or.jp/download/
[23]
Hiura, M., Kido, S. and Shouno, H. (2005) Development of Pulmonary Nodule Detection Method on Chest Radiographs. Medical Imaging Technology, 23, 250-258.
[24]
Brownlee, J. (2021) Gentle Introduction to the Adam Optimization Algorithm for Deep Learning. Machine Learning Mastery. https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/
[25]
Musoro, J.Z., Zwinderman, A.H., Puhan, M.A., Riet, G.T. and Geskus, R.B. (2014) Validation of Prediction Models Based on Lasso Regression with Multiply Imputed Data. BMC Medical Research Methodology, 14, Article No. 116. https://doi.org/10.1186/1471-2288-14-116
[26]
Oh, Y., Park, S. and Ye, J.C. (2020) Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets. IEEE Transactions on Medical Imaging, 39, 2688-2700. https://doi.org/10.1109/TMI.2020.2993291
