Explainable Deep Learning for Lung Disease Detection on Chest X-ray Images Using Local Interpretable Model-Agnostic Explanations (LIME)
Downloads
Artificial Intelligence (AI) is increasingly being applied in the healthcare field through Machine Learning (ML) and Deep Learning (DL) models. However, the complexity of modern black-box models creates a need for transparent interpretation methods. Explainable AI (XAI) emerges to bridge this gap by providing better understanding of model performance. This study implements the Local Interpretable Model-agnostic Explanations (LIME) method to visualize the classification results of a DL model based on the ResNet18 architecture on Chest X-ray (CXR) images across three classes: normal, COVID-19, and pneumonia. The model achieved a precision of 97%, recall of 97%, and F1-score of 97%, with an accuracy of 98%. LIME visualizations highlight the image regions that significantly contribute to the classification and effectively distinguish among the three classes. The results of this study demonstrate that applying XAI specifically LIME with a ResNet18-based DL model can provide interpretability in CXR image classification tasks.
V. Hassija et al., “Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence,” Jan. 01, 2024, Springer. doi: 10.1007/s12559-023-10179-8.
I. Md. D. Maysanjaya, “Classification of Pneumonia in Lung X-ray Images with Convolutional Neural Network,” National Journal of Electrical Engineering and Information Technology, vol. 9, no. 2, pp. 190–195, 2020, doi: 10.22146/jnteti.v9i2.66.
M. Harahap, Em Manuel Laia, Lilis Suryani Sitanggang, Melda Sinaga, Daniel Franci Sihombing, and Amir Mahmud Husein, “Detection of Covid-19 Disease in X-ray Images-Ray Images Using a Convolutional Neural Network (CNN) Approach," Jurnal RESTI (Engineering Systems and Information Technology), vol. 6, no. 1, pp. 70–77, Feb. 2022, doi: 10.29207/resti.v6i1.3373.
A. Saxena, “An Introduction to Convolutional Neural Networks,” Int J Res Appl Sci Eng Technol, vol. 10, no. 12, pp. 943–947, 2022, doi: 10.22214/ijraset.2022.47789.
F. Xoliyarov, S. Gulomov, and S. Bozorov, “The Impact of Artificial Neural Network Architecture on Network Attack Detection,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Dec. 2023, pp. 532–539. doi: 10.1145/3644713.3644792.
S. A. Hasanah, A. A. Pravitasari, A. S. Abdullah, and I. N. Yulita, “applied sciences A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image,” 2023.
S. Sarp et al., “An XAI approach for COVID-19 detection using transfer learning with X-ray images,” Heliyon, vol. 9, no. 4, p. e15137, 2023, doi: 10.1016/j.heliyon.2023.e15137.
F. Bodria, F. Giannotti, R. Guidotti, F. Naretto, D. Pedreschi, and S. Rinzivillo, “Benchmarking and survey of explanation methods for black box models,” Data Mining and Knowledge Discovery, vol. 37, no. 5, pp. 1719–1778, Sep. 2023, doi: 10.1007/s10618-023-00933-9.
S. Sharma, K. Kaushik, R. Sharma, and N. Chaturvedi, “Ijfans International Journal Of Food And Nutritional Sciences Explainable Artificial Intelligence (XAI),” 2012.
M. Rehman Zafar and N. Khan, “machine learning & knowledge extraction Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability,” 2021, doi: 10.3390/make.
M. Toğaçar, N. Muzoğlu, B. Ergen, B. S. B. Yarman, and A. M. Halefoğlu, “Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs,” Biomed Signal Process Control, vol. 71, no. May 2021, pp. 0–3, 2022, doi: 10.1016/j.bspc.2021.103128.
N. Nigar, M. Umar, M. K. Shahzad, S. Islam, and D. Abalo, “A Deep Learning Approach Based on Explainable Artificial Intelligence for Skin Lesion Classification,” IEEE Access, vol. 10, no. October, pp. 113715–113725, 2022, doi: 10.1109/ACCESS.2022.3217217.
S. Kumar et al., “LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images,” Int J Imaging Syst Technol, vol. 32, no. 5, pp. 1464–1480, Sep. 2022, doi: 10.1002/ima.22770.
O. Muraina, “IDEAL DATASET SPLITTING RATIOS IN MACHINE LEARNING ALGORITHMS: GENERAL CONCERNS FOR DATA SCIENTISTS AND DATA ANALYSTS.” [Online]. Available: https://www.researchgate.net/publication/358284895
P. Mohammadinasab, “Pneumonia Detection Using Deep Convolutional Neural Networks,” no. September, 2023, doi: 10.13140/RG.2.2.25567.02720.
I. Salehin and D. K. Kang, “A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain,” Electronics (Switzerland), vol. 12, no. 14, 2023, doi: 10.3390/electronics12143106.
M. Reyad, A. M. Sarhan, and M. Arafa, “A modified Adam algorithm for deep neural network optimization,” Neural Comput Appl, vol. 35, no. 23, pp. 17095–17112, 2023, doi: 10.1007/s00521-023-08568-z.
H. Iiduka, “Appropriate Learning Rates of Adaptive Learning Rate Optimization Algorithms for Training Deep Neural Networks,” IEEE Trans Cybern, vol. 52, no. 12, pp. 13250–13261, 2022, doi: 10.1109/TCYB.2021.3107415.
N. Das and S. Das, “Epoch and accuracy based empirical study for cardiac MRI segmentation using deep learning technique,”PeerJ, vol. 11, 2023, doi: 10.7717/peerj.14939.
W. M. Oboya, A. W. Gichuhi, and A. Wanjoya, “A Hybrid DNN-RBFNN Model for Intrusion Detection System,” Journal of Data Analysis and Information Processing, vol. 11, no. 04, pp. 371–387, 2023, doi: 10.4236/jdaip.2023.114019.
V. R. Mishra, “Image classification of Cow Teat by implementing Convolution Neural Network using PyTorch and Residual Block Image classification of Cow Teat by implementing Convolution Neural Network using PyTorch and Residual Block,” no. November, pp. 0–4, 2023.
D. Krstinić, M. Braović, L. Šerić, and D. Božić-Štulić, “Multi-label Classifier Performance Evaluation with Confusion Matrix,” Academy and Industry Research Collaboration Center (AIRCC), Jun. 2020, pp. 01–14. doi: 10.5121/csit.2020.100801.
G. Schwalbe and B. Finzel, “A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts,” Data Min Knowl Discov, vol. 38, no. 5, pp. 3043–3101, 2024, doi: 10.1007/s10618-022-00867-8.
Y.-S. Lin, W.-C. Lee, and Z. B. Celik, “What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors,” Sep. 2020, [Online]. Available: http://arxiv.org/abs/2009.10639
A. Chaddad, J. Peng, J. Xu, and A. Bouridane, “Survey of Explainable AI Techniques in Healthcare,” Jan. 01, 2023, MDPI. doi: 10.3390/s23020634.
E. G. Cervantes and W. Y. Chan, “LIME-Enabled Investigation of Convolutional Neural Network Performances in COVID-19 Chest X-Ray Detection,” Canadian Conference on Electrical and Computer Engineering, vol. 2021-Septe, pp. 1–6, 2021, doi: 10.1109/CCECE53047.2021.9569029.
P. P. Angelov, E. A. Soares, R. Jiang, N. I. Arnold, and P. M. Atkinson, “Explainable artificial intelligence: an analytical review,”Wiley Interdiscip Rev Data Min Knowl Discov, vol. 11, no. 5, Sep. 2021, doi: 10.1002/widm.1424.
E. Tjoa and C. Guan, “A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI,” IEEE Trans Neural Netw Learn Syst, vol. 32, no. 11, pp. 4793–4813, Nov. 2021, doi: 10.1109/TNNLS.2020.3027314.
F. Bodria, F. Giannotti, R. Guidotti, F. Naretto, D. Pedreschi, and S. Rinzivillo, Benchmarking and survey of explanation methods for black box models, vol. 37, no. 5. Springer US, 2023. doi: 10.1007/s10618-023-00933-9.
F. M. Talaat and S. A. Gamel, “RL based hyper-parameters optimization algorithm (ROA) for convolutional neural network,”J Ambient Intell Humaniz Comput, vol. 14, no. 10, pp. 13349–13359, Oct. 2023, doi: 10.1007/s12652-022-03788-y.
