Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning
<b>Background/Objectives</b>: Liver cirrhosis is a critical chronic condition with increasing global mortality and morbidity rates, emphasizing the necessity for early and accurate diagnosis. This study proposes a comprehensive deep-learning framework for the automatic diagnosis and stag...
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MDPI AG
2025-05-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/9/1177 |
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| author | Serkan Savaş |
| author_facet | Serkan Savaş |
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| collection | DOAJ |
| description | <b>Background/Objectives</b>: Liver cirrhosis is a critical chronic condition with increasing global mortality and morbidity rates, emphasizing the necessity for early and accurate diagnosis. This study proposes a comprehensive deep-learning framework for the automatic diagnosis and staging of liver cirrhosis using T2-weighted MRI images. <b>Methods</b>: The methodology integrates stacked ensemble learning, multi-task learning (MTL), and transfer learning within an explainable artificial intelligence (XAI) context to improve diagnostic accuracy, reliability, and transparency. A hybrid model combining multiple pre-trained convolutional neural networks (VGG16, MobileNet, and DenseNet121) with XGBoost as a meta-classifier demonstrated robust performance in binary classification between healthy and cirrhotic cases. <b>Results</b>: The model achieved a mean accuracy of 96.92%, precision of 95.12%, recall of 98.93%, and F1-score of 96.98% across 10-fold cross-validation. For staging (mild, moderate, and severe), the MTL framework reached a main task accuracy of 96.71% and an average AUC of 99.81%, with a powerful performance in identifying severe cases. Grad-CAM visualizations reveal class-specific activation regions, enhancing the transparency and trust in the model’s decision-making. The proposed system was validated using the CirrMRI600+ dataset with a 10-fold cross-validation strategy, achieving high accuracy (AUC: 99.7%) and consistent results across folds. <b>Conclusions</b>: This research not only advances State-of-the-Art diagnostic methods but also addresses the black-box nature of deep learning in clinical applications. The framework offers potential as a decision-support system for radiologists, contributing to early detection, effective staging, personalized treatment planning, and better-informed treatment planning for liver cirrhosis. |
| format | Article |
| id | doaj-art-2cd3cf47b5454f02af22bebdb7b7f489 |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-05-01 |
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| series | Diagnostics |
| spelling | doaj-art-2cd3cf47b5454f02af22bebdb7b7f4892025-08-20T02:59:14ZengMDPI AGDiagnostics2075-44182025-05-01159117710.3390/diagnostics15091177Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task LearningSerkan Savaş0Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kırıkkale University, Kırıkkale 71450, Türkiye<b>Background/Objectives</b>: Liver cirrhosis is a critical chronic condition with increasing global mortality and morbidity rates, emphasizing the necessity for early and accurate diagnosis. This study proposes a comprehensive deep-learning framework for the automatic diagnosis and staging of liver cirrhosis using T2-weighted MRI images. <b>Methods</b>: The methodology integrates stacked ensemble learning, multi-task learning (MTL), and transfer learning within an explainable artificial intelligence (XAI) context to improve diagnostic accuracy, reliability, and transparency. A hybrid model combining multiple pre-trained convolutional neural networks (VGG16, MobileNet, and DenseNet121) with XGBoost as a meta-classifier demonstrated robust performance in binary classification between healthy and cirrhotic cases. <b>Results</b>: The model achieved a mean accuracy of 96.92%, precision of 95.12%, recall of 98.93%, and F1-score of 96.98% across 10-fold cross-validation. For staging (mild, moderate, and severe), the MTL framework reached a main task accuracy of 96.71% and an average AUC of 99.81%, with a powerful performance in identifying severe cases. Grad-CAM visualizations reveal class-specific activation regions, enhancing the transparency and trust in the model’s decision-making. The proposed system was validated using the CirrMRI600+ dataset with a 10-fold cross-validation strategy, achieving high accuracy (AUC: 99.7%) and consistent results across folds. <b>Conclusions</b>: This research not only advances State-of-the-Art diagnostic methods but also addresses the black-box nature of deep learning in clinical applications. The framework offers potential as a decision-support system for radiologists, contributing to early detection, effective staging, personalized treatment planning, and better-informed treatment planning for liver cirrhosis.https://www.mdpi.com/2075-4418/15/9/1177liver cirrhosisexplainable artificial intelligence (XAI)stacked ensemble learningmulti-task learning (MTL)transfer learningGrad-CAM |
| spellingShingle | Serkan Savaş Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning Diagnostics liver cirrhosis explainable artificial intelligence (XAI) stacked ensemble learning multi-task learning (MTL) transfer learning Grad-CAM |
| title | Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning |
| title_full | Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning |
| title_fullStr | Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning |
| title_full_unstemmed | Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning |
| title_short | Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning |
| title_sort | explainable artificial intelligence for diagnosis and staging of liver cirrhosis using stacked ensemble and multi task learning |
| topic | liver cirrhosis explainable artificial intelligence (XAI) stacked ensemble learning multi-task learning (MTL) transfer learning Grad-CAM |
| url | https://www.mdpi.com/2075-4418/15/9/1177 |
| work_keys_str_mv | AT serkansavas explainableartificialintelligencefordiagnosisandstagingoflivercirrhosisusingstackedensembleandmultitasklearning |