Savaş Serkan
Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Kırıkkale University, Kırıkkale 71450, Türkiye.
Diagnostics (Basel). 2025 May 6;15(9):1177. doi: 10.3390/diagnostics15091177.
: 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. : 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. : 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. : 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.
肝硬化是一种严重的慢性疾病，全球死亡率和发病率不断上升，这凸显了早期准确诊断的必要性。本研究提出了一种综合深度学习框架，用于使用T2加权磁共振成像（MRI）图像对肝硬化进行自动诊断和分期。该方法在可解释人工智能（XAI）背景下集成了堆叠集成学习、多任务学习（MTL）和迁移学习，以提高诊断准确性、可靠性和透明度。一种将多个预训练卷积神经网络（VGG16、MobileNet和DenseNet121）与XGBoost作为元分类器相结合的混合模型在健康与肝硬化病例的二元分类中表现出强大性能。该模型在10折交叉验证中平均准确率达到96.92%，精确率为95.12%，召回率为98.93%，F1分数为96.98%。对于分期（轻度、中度和重度），MTL框架的主要任务准确率达到96.71%，平均曲线下面积（AUC）为99.81%，在识别严重病例方面表现出色。Grad-CAM可视化揭示了特定类别的激活区域，增强了对模型决策的透明度和信任度。所提出的系统使用CirrMRI600+数据集并采用10折交叉验证策略进行了验证，实现了高精度（AUC：99.7%）且各折结果一致。这项研究不仅推动了当前最先进的诊断方法，还解决了深度学习在临床应用中的黑箱性质问题。该框架有望作为放射科医生的决策支持系统，有助于肝硬化的早期检测、有效分期、个性化治疗规划以及更明智的治疗规划。
