PDF(Pubmed)
Abstract：
A recent global health crisis, COVID-19 is a significant global health crisis that has profoundly affected lifestyles. The detection of such diseases from similar thoracic anomalies using medical images is a challenging task. Thus, the requirement of an end-to-end automated system is vastly necessary in clinical treatments. In this way, the work proposes a Squeeze-and-Excitation Attention-based ResNet50 (SEA-ResNet50) model for detecting COVID-19 utilizing chest X-ray data. Here, the idea lies in improving the residual units of ResNet50 using the squeeze-and-excitation attention mechanism. For further enhancement, the Ranger optimizer and adaptive Mish activation function are employed to improve the feature learning of the SEA-ResNet50 model. For evaluation, two publicly available COVID-19 radiographic datasets are utilized. The chest X-ray input images are augmented during experimentation for robust evaluation against four output classes namely normal, pneumonia, lung opacity, and COVID-19. Then a comparative study is done for the SEA-ResNet50 model against VGG-16, Xception, ResNet18, ResNet50, and DenseNet121 architectures. The proposed framework of SEA-ResNet50 together with the Ranger optimizer and adaptive Mish activation provided maximum classification accuracies of 98.38% (multiclass) and 99.29% (binary classification) as compared with the existing CNN architectures. The proposed method achieved the highest Kappa validation scores of 0.975 (multiclass) and 0.98 (binary classification) over others. Furthermore, the visualization of the saliency maps of the abnormal regions is represented using the explainable artificial intelligence (XAI) model, thereby enhancing interpretability in disease diagnosis.
摘要：
最近的全球健康危机，COVID-19是一场严重的全球健康危机，深刻影响了人们的生活方式。使用医学图像从类似的胸部异常中检测此类疾病是一项具有挑战性的任务。因此，在临床治疗中，端到端自动化系统的要求是非常必要的。这样,这项工作提出了一种基于挤压和激发注意力的ResNet50（SEA-ResNet50）模型，用于利用胸部X射线数据检测COVID-19。这里,这个想法在于使用挤压和激励注意力机制改进ResNet50的剩余单位。为了进一步增强，Ranger优化器和自适应Mish激活函数用于改进SEA-ResNet50模型的特征学习。为了评估，利用了两个公开的COVID-19射线照相数据集。在实验期间，胸部X射线输入图像被增强，以针对四个输出类别进行稳健评估，即正常，肺炎,肺混浊,和COVID-19。然后对SEA-ResNet50模型与VGG-16，Xception，ResNet18、ResNet50和DenseNet121体系结构。与现有的CNN架构相比，所提出的SEA-ResNet50框架以及Ranger优化器和自适应Mish激活提供了98.38％（多类）和99.29％（二元分类）的最大分类精度。所提出的方法比其他方法获得了0.975（多分类）和0.98（二元分类）的最高Kappa验证分数。此外，使用可解释人工智能(XAI)模型来表示异常区域的显著性图的可视化，从而提高疾病诊断的可解释性。
