用AI模型提升口腔癌病理诊断准确率，结合解释技术增强可信度。
Deep Learning Approach for Enhancing Oral Squamous Cell Carcinoma with LIME Explainable AI Technique
- 采用EfficientNetB3等深度学习模型分析病理图像
- 模型准确率达98.33%，显著优于其他架构
- 结合LIME解释技术，提升AI决策可读性
本研究旨在通过深度学习模型提升口腔鳞状细胞癌（OSCC）的诊断性能，基于纵向队列研究与口腔癌病理影像数据库进行分析。数据集包含5192张图像（正常2435张，OSCC 2511张），采用分层抽样分割为训练、验证和测试集，整体比例接近均衡。评估了ResNet101、DenseNet121、VGG16和EfficientNetB3四种模型，其中EfficientNetB3表现最佳，准确率为98.33%，F1得分为0.9844，且计算开销显著更低。其次为DenseNet121，准确率90.24%，F1得分为90.45%。进一步使用局部可解释模型无关解释（LIME）技术解析EfficientNetB3的预测依据，提升结果的可解释性与可信度。研究证实EfficientNetB3在OSCC诊断中具有优越潜力，并为临床应用提供重要基础。
原文摘要 · Abstract (English)
The goal of the present study is to analyze an application of deep learning models in order to augment the diagnostic performance of oral squamous cell carcinoma (OSCC) with a longitudinal cohort study using the Histopathological Imaging Database for oral cancer analysis. The dataset consisted of 5192 images (2435 Normal and 2511 OSCC), which were allocated between training, testing, and validation sets with an estimated ratio repartition of about 52% for the OSCC group, and still, our performance measure was validated on a combination set that contains almost equal number of sample in this use case as entire database have been divided into half using stratified splitting technique based again near binary proportion but total distribution was around even. We selected four deep-learning architectures for evaluation in the present study: ResNet101, DenseNet121, VGG16, and EfficientnetB3. EfficientNetB3 was found to be the best, with an accuracy of 98.33% and F1 score (0.9844), and it took remarkably less computing power in comparison with other models. The subsequent one was DenseNet121, with 90.24% accuracy and an F1 score of 90.45%. Moreover, we employed the Local Interpretable Model-agnostic Explanations (LIME) method to clarify why EfficientNetB3 made certain decisions with its predictions to improve the explainability and trustworthiness of results. This work provides evidence for the possible superior diagnosis in OSCC activated from the EfficientNetB3 model with the explanation of AI techniques such as LIME and paves an important groundwork to build on towards clinical usage.
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