论文标题
从可解释的过滤器到具有可解释的人工智能的卷积神经网络的预测
From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence
论文作者
论文摘要
卷积神经网络（CNN）以其出色的功能提取能力而闻名，可以从数据中学习模型，但被用作黑匣子。对卷积滤液和相关特征的解释可以帮助建立对CNN的理解，以区分各种类别。在这项工作中，我们专注于CNN模型称为CNNEXPLAIN的CNN模型，该模型用于COVID-19和非CoVID-19分类，重点关注卷积过滤器的特征的解释性，以及这些功能如何有助于分类。具体而言，我们使用了各种可解释的人工智能（XAI）方法，例如可视化，SmoothGrad，Grad-Cam和Lime来提供卷积滤液的解释，以及相关特征及其在分类中的作用。我们已经分析了使用干咳嗽光谱图的这些方法的解释。从石灰，光滑果实和GRAD-CAM获得的解释结果突出了不同频谱图的重要特征及其与分类的相关性。
Convolutional neural networks (CNN) are known for their excellent feature extraction capabilities to enable the learning of models from data, yet are used as black boxes. An interpretation of the convolutional filtres and associated features can help to establish an understanding of CNN to distinguish various classes. In this work, we focus on the explainability of a CNN model called as cnnexplain that is used for Covid-19 and non-Covid-19 classification with a focus on the interpretability of features by the convolutional filters, and how these features contribute to classification. Specifically, we have used various explainable artificial intelligence (XAI) methods, such as visualizations, SmoothGrad, Grad-CAM, and LIME to provide interpretation of convolutional filtres, and relevant features, and their role in classification. We have analyzed the explanation of these methods for Covid-19 detection using dry cough spectrograms. Explanation results obtained from the LIME, SmoothGrad, and Grad-CAM highlight important features of different spectrograms and their relevance to classification.
