MOZART：使用胸部X射线图像进行COVID-19检测的集成方法
MOZART: Ensembling Approach for COVID-19 Detection using Chest X-Ray Imagery
AI总结：
针对COVID-19胸部X射线检测中假阳性和假阴性较高的问题，提出集成学习框架MOZART，通过结合InceptionV3、Xception和ResNet50的预测训练浅层神经网络，在关键指标上优于单一模型，准确率达99.17%。
摘要：
COVID-19引发了全球大流行，使医疗保健系统不堪重负。早期和准确的检测对于控制病毒传播至关重要。虽然逆转录聚合酶链反应测试是诊断的金标准，但其可用性有限、处理时间长且假阴性率极高，促使人们探索替代方法。胸部X射线成像已成为识别COVID-19相关肺部异常的有价值的非侵入性工具。传统的卷积神经网络（CNN）实现了令人印象深刻的准确性，但在关键的医疗应用中，需要更稳健的解决方案来最小化假阳性和假阴性。因此，我们引入了MOZART框架，这是一种增强病毒检测的集成学习方法。我们在包含3,616张COVID-19和3,616张健康图像的平衡胸部X射线数据集上训练了三种CNN架构：InceptionV3、Xception和ResNet50。每个模型都经历了单独的预处理流程，例如将输入归一化到-1到1的范围。数据集被划分为70%用于训练，20%用于验证，10%用于测试。在训练各个模型之后，我们在这些预测结果上训练了一个浅层神经网络，以提供最终的预测。我们的结果表明，包含子实验MOZART1和MOZART2的MOZART框架在关键指标上优于单个CNN模型。它达到了99.17%的准确率和99.16%的F1分数。MOZART1在最小化假阳性方面表现优异，而MOZART2在减少假阴性方面更佳。这项工作表明，MOZART框架可以提高AI驱动的医学成像任务的可靠性，并应进一步探索用于其他肺部疾病。
英文摘要：
COVID-19, has led to a global pandemic that strained the healthcare systems. Early and accurate detection is crucial for controlling the spread of the virus. While reverse transcription polymerase chain reaction test is the gold standard for diagnosis, it's limited availability, long processing times and extremely high false negative rate, have prompted the exploration of alternative methods. Chest Xray imaging has emerged as a valuable, non invasive tool for identifying COVID-19 related lung abnormalities. Traditional convolutional neural networks (CNNs) achieve impressive accuracy, but there is a need for more robust solutions to minimize false positives and negatives in critical medical applications. Thus We introduce the MOZART framework, an ensemble learning approach that enhances the virus detection. We trained three CNN architectures InceptionV3, Xception, and ResNet50 on a balanced chest X-ray dataset of 3,616 COVID-19 and 3,616 healthy images. Each model underwent a separate preprocessing pipeline, such as normalizing inputs to a range of -1 to 1. The dataset was split into 70% for training, 20% for validation, and 10% for testing, after training the individual models, we trained a shallow neural network on the predictions and to provide a us with the final predictions. Our results show that the MOZART framework with it's sub-experiments MOZART1 and MOZART2 outperforms individual CNN models in key metrics. It achieved an accuracy of 99.17% and an F1 score of 99.16%. MOZART1 excels at minimizing false positives, while MOZART2 is better for reducing false negatives. This work suggests that the MOZART framework can improve reliability in AI-driven medical imaging tasks and should be explored further for other lung diseases.
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