AppendiGrade：一种结合高斯模糊与Grad-CAM的可解释人工智能（XAI）增强深度学习框架，用于超声图像中阑尾炎的分级

中文摘要 AI 辅助

阑尾炎是全球最常见的腹部急症之一，需及时诊断与治疗以避免危及生命的状况。然而，准确区分复杂病例（如穿孔或脓肿形成）与非复杂性阑尾炎仍是重大临床挑战。在各类方法中，超声因无辐射暴露，是更安全、更具成本效益的诊断技术。本研究开发了一种能从超声图像自动检测复杂性阑尾炎的先进系统，使用包含4679张超声图像的数据集（分为穿孔、脓肿、急性、阑尾粪石、正常5类）进行所提模型的训练与测试。采用DenseNet201、InceptionV3、ConvNextTiny、VGG19这4种预训练深度学习模型检测和分类复杂性阑尾炎。初始配置下，InceptionV3准确率为69.21%，位列第二。因原始图像性能欠佳，应用了图像预处理、超参数调优、模型微调及图像锐化等进一步优化技术，这些改进显著提升了模型性能，InceptionV3准确率达95.58%。随后用梯度加权类激活映射（Grad-CAM）解释模型性能，该方法生成模型预测感染区域的热力图，可大幅简化与专家的交叉验证流程。

英文摘要

Appendicitis is one of the most common abdominal emergencies worldwide and requires prompt diagnosis and treatment to prevent life-threatening conditions. However, accurately differentiating complicated cases, such as perforation or abscess formation, from uncomplicated appendicitis remains a significant clinical challenge. Among other methods, ultrasound is a safer and more cost-efficient diagnostic technique because of the lack of radiation exposure. In this research, an advanced system capable of automatically detecting complicated appendicitis from ultrasound images was developed. A dataset consisting of 4679 ultrasound images with 5 classes, namely perforated, abscess, acute, appendicolith, and normal, was used for the proposed model training and testing. Four pretrained deep learning models, DenseNet201, InceptionV3, ConvNextTiny, and VGG19, have been employed for detecting and classifying complicated appendicitis. In the initial configuration, InceptionV3 achieved the second highest accuracy, with a value of 69.21%. Owing to suboptimal performance with raw images, further optimization techniques, including image preprocessing, hyperparameter tuning, model fine-tuning, and image sharpening, were applied. These enhancements significantly improved the model's performance, with an accuracy of 95.58% for InceptionV3. The model performance is then explained with gradient-weighted class activation mapping (Grad-CAM), which creates a heatmap of the regions responsible for the model's prediction of the infected areas. This could make crosschecking with experts much easier.

发表机构

• Ca’ Foscari University of Venice（威尼斯大学）
• East West University（东西方大学）
• Tokyo International University（东京国际大学）
• Southern Cross University（南十字星大学）

机构由 AI 辅助整理，请以论文原文为准。