Interpretable AI for Liver Cancer Detection: Cascaded CNN & GLCM Integration
Received: 7 January 2025 | Revised: 27 January 2025 | Accepted: 1 February 2025 | Online: 3 April 2025
Corresponding author: Jayasimha Sondekoppa Rajkumar
Abstract
Liver cancer has significantly high mortality, especially in regions such as Africa and Asia. Early detection enhances treatment options, but indications are frequently not apparent until advanced stages. This research introduces an explainable AI (XAI) approach using a cascaded Convolutional Neural Network (CNN) combined with Gray Level Co-occurrence Matrix (GLCM)-based texture features to segregate non-cancerous from malicious tumors. The CLD system was used for assessment, and the approach was examined using the TCIA dataset, demonstrating higher accuracy and interpretability compared to prevailing techniques. XAI methods, such as feature importance and model visualization, were employed to provide details on the decision-making process of the model, ensuring transparency and reliability in clinical applications.
Keywords:
Hepatocellular Carcinoma (HCC), Metastatic Carcinoma (MC), Convolutional Neural Network (CNN), Machine Learning (ML), Explainable Artificial Intelligence (XAI), Gray Level Cooccurrence Matrix (GLCM)Downloads
References
M. B. Moin, F. T. J. Faria, S. Saha, B. K. Rafa, and M. S. Alam, "Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification." arXiv, May 14, 2024.
C. Bouron et al., "Prognostic Value of Metabolic, Volumetric and Textural Parameters of Baseline [18F]FDG PET/CT in Early Triple-Negative Breast Cancer," Cancers, vol. 14, no. 3, Jan. 2022, Art. no. 637.
G. S. Sannala, K. V. G. Rohith, A. G. Vyas, and C. R. Kavitha, "Explainable Artificial Intelligence-Based Disease Prediction with Symptoms Using Machine Learning Models," in IoT Based Control Networks and Intelligent Systems, vol. 789, P. P. Joby, M. S. Alencar, and P. Falkowski-Gilski, Eds. Singapore: Springer Nature Singapore, 2024, pp. 523–538.
A. Chaddad, J. Peng, J. Xu, and A. Bouridane, "Survey of Explainable AI Techniques in Healthcare," Sensors, vol. 23, no. 2, Jan. 2023, Art. no. 634.
Y. Abas Mohamed, B. E. Khoo, M. Shahrimie Mohd Asaari, M. Ezane Aziz, and F. Rahiman Ghazali, "Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review," International Journal of Medical Informatics, vol. 193, Jan. 2025, Art. no. 105689.
P. Barra, A. Della Greca, I. Amaro, A. Tortora, and M. Staffa, "A Comparative Analysis of XAI Techniques for Medical Imaging: Challenges and Opportunities," in 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal, Dec. 2024, pp. 6782–6788.
O. Sarkar et al., "Multi-Scale CNN: An Explainable AI-Integrated Unique Deep Learning Framework for Lung-Affected Disease Classification," Technologies, vol. 11, no. 5, Sep. 2023, Art. no. 134.
B. C. Anil and P. Dayananda, "Automatic Liver Tumor Segmentation based on Multi-level Deep Convolutional Networks and Fractal Residual Network," IETE Journal of Research, vol. 69, no. 4, pp. 1925–1933, May 2023.
N. P. Nelaturi, V. Rajesh, and I. Syed, "Real-Time Liver Tumor Detection with a Multi-Class Ensemble Deep Learning Framework," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 16103–16108, Oct. 2024.
N. C. Kundur, B. C. Anil, P. M. Dhulavvagol, R. Ganiger, and B. Ramadoss, "Pneumonia Detection in Chest X-Rays using Transfer Learning and TPUs," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11878–11883, Oct. 2023.
M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," International Journal of Computer Vision, vol. 1, no. 4, pp. 321–331, Jan. 1988.
P. F. Christ et al., "Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks." arXiv, Feb. 23, 2017.
Y. Yuan, "Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation." arXiv, Oct. 12, 2017.
A. L. Simpson et al., "Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases," Scientific Data, vol. 11, no. 1, Feb. 2024, Art. no. 172.
K. Clark et al., "The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository," Journal of Digital Imaging, vol. 26, no. 6, pp. 1045–1057, Dec. 2013.
G. Chlebus, H. Meine, J. H. Moltz, and A. Schenk, "Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering." arXiv, Jun. 27, 2017.
X. Han, "Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method," Medical Physics, vol. 44, no. 4, pp. 1408–1419, Apr. 2017.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 770–778.
C. Peng, X. Zhang, G. Yu, G. Luo, and J. Sun, "Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, Jul. 2017, pp. 1743–1751.
P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, "Image-to-Image Translation with Conditional Adversarial Networks," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, Jul. 2017, pp. 5967–5976.
S. Saha Roy, S. Roy, P. Mukherjee, and A. Halder Roy, "An automated liver tumour segmentation and classification model by deep learning based approaches," Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol. 11, no. 3, pp. 638–650, May 2023.
Downloads
How to Cite
License
Copyright (c) 2025 Bellary Chiterki Anil, Jayasimha Sondekoppa Rajkumar, Arun Kumar Gowdru, Kiran P. Rakshitha, Samitha Khaiyum, Basavaiah Lathamani, Balakrishnan Ramadoss
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
