用孟加拉数据提升肺部X光片新冠检测准确率，结合可解释AI提升诊断可信度。
A Comprehensive Analysis of COVID-19 Detection Using Bangladeshi Data and Explainable AI
- 基于4350张孟加拉胸片，用VGG19模型实现98%检测准确率。
- 采用SMOTE缓解类别不平衡，结合LIME揭示模型决策关键区域。
- 适合医疗AI开发者与放射科医生，助力临床诊断透明化。
新冠疫情迅速传播，全球数以百万计人受感染。截至2024年4月，孟加拉国已有约29,495例死亡和超过200万确诊病例。本研究利用包含4,350张胸片的孟加拉数据集，涵盖正常、肺部阴影、新冠及病毒性肺炎四类，应用机器学习、深度学习与迁移学习模型进行检测。其中VGG19模型达到98%准确率。通过SMOTE处理类别不平衡问题，并使用LIME对模型预测进行解释，揭示影响分类的关键区域与特征。该研究展示了可解释AI在提升模型透明性与可靠性方面的作用，有助于提高胸部X光图像中新冠检测的有效性。
原文摘要 · Abstract (English)
COVID-19 is a rapidly spreading and highly infectious virus which has triggered a global pandemic, profoundly affecting millions across the world. The pandemic has introduced unprecedented challenges in public health, economic stability, and societal structures, necessitating the implementation of extensive and multifaceted health interventions globally. It had a tremendous impact on Bangladesh by April 2024, with around 29,495 fatalities and more than 2 million confirmed cases. This study focuses on improving COVID-19 detection in CXR images by utilizing a dataset of 4,350 images from Bangladesh categorized into four classes: Normal, Lung-Opacity, COVID-19 and Viral-Pneumonia. ML, DL and TL models are employed with the VGG19 model achieving an impressive 98% accuracy. LIME is used to explain model predictions, highlighting the regions and features influencing classification decisions. SMOTE is applied to address class imbalances. By providing insight into both correct and incorrect classifications, the study emphasizes the importance of XAI in enhancing the transparency and reliability of models, ultimately improving the effectiveness of detection from CXR images.
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