融合血液检测与超声影像，用投票机制提升肝纤维化诊断准确率
Hybrid Approach Combining Ultrasound and Blood Test Analysis with a Voting Classifier for Accurate Liver Fibrosis and Cirrhosis Assessment
- 结合血液指标概率与深度学习超声图像预测结果，采用投票机制融合决策
- 模型整体准确率达92.5%，显著优于单一数据源方法
- 适合临床早期肝病筛查，为非侵入式诊断提供新思路
肝硬化是一种隐匿性疾病，表现为正常肝组织被纤维瘢痕组织替代，引发严重健康问题。传统诊断依赖肝穿刺活检，具有侵入性，难以用于常规筛查。本文提出一种混合模型，融合机器学习技术、临床数据与超声扫描，以提升肝纤维化和肝硬化的检测准确性。该模型将固定血液检测概率与基于DenseNet-201的超声图像深度学习预测结果相结合。联合混合模型达到92.5%的准确率，验证了该方法在提升诊断精度和支持肝病早期干预方面的可行性。
原文摘要 · Abstract (English)
Liver cirrhosis is an insidious condition involving the substitution of normal liver tissue with fibrous scar tissue and causing major health complications. The conventional method of diagnosis using liver biopsy is invasive and, therefore, inconvenient for use in regular screening. In this paper,we present a hybrid model that combines machine learning techniques with clinical data and ultrasoundscans to improve liver fibrosis and cirrhosis detection accuracy is presented. The model integrates fixed blood test probabilities with deep learning model predictions (DenseNet-201) for ultrasonic images. The combined hybrid model achieved an accuracy of 92.5%. The findings establish the viability of the combined model in enhancing diagnosis accuracy and supporting early intervention in liver disease care.
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