Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{187023,
        author = {S.SINDUJA and SUBASRI D R and K.SIVAPRIYA},
        title = {A Survey Paper on Hepastage an Accessible Data – Driven Approach for Interpretable Cirrhosis Progression Categorization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3934-3944},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187023},
        abstract = {The increasing prevalence of liver cirrhosis worldwide has created an urgent need for accurate and early-stage classification systems to support timely diagnosis and treatment planning. Traditional clinical methods often face challenges due to subtle radiological variations and overlapping biomarker patterns across cirrhosis stages. To address these challenges, this research proposes a deep learning–based framework that leverages convolutional neural networks (CNNs) for medical image analysis, combined with key liver biomarkers to enhance predictive accuracy. This study presents a comprehensive approach to automated cirrhosis stage classification by utilizing advanced CNN architectures for feature extraction from radiological images, while simultaneously integrating biomarker data to improve classification robustness. The framework applies image preprocessing, data augmentation, and transfer learning to maximize model generalization. Evaluation is performed on benchmark liver datasets containing radiological images alongside clinical biomarkers. Performance is assessed using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices to ensure clinical reliability. The proposed system demonstrates strong potential in reducing misdiagnosis, supporting radiologists in clinical decision-making. This work highlights the significant role of deep learning in advancing medical diagnostics by integrating image-based and clinical biomarker-driven analysis for accurate liver cirrhosis stage prediction.},
        keywords = {Liver Cirrhosis, Deep Learning (DL), Convolutional Neural Networks (CNN), Biomarkers, Medical Image Analysis, Computer-Aided Diagnosis.},
        month = {November},
        }