论文标题
COVID-19检测的深层模型的可解释性分析
Interpretability Analysis of Deep Models for COVID-19 Detection
论文作者
论文摘要
在Covid-19-19大流行期间，几个研究领域加入了减轻SARS-COV-2造成的损害的努力。在本文中，我们介绍了基于卷积神经网络的模型的可解释性分析，用于音频中的Covid-19检测。我们研究哪些功能对于模型决策过程很重要，研究频谱图，F0，F0标准偏差，性别和年龄很重要。随后，我们通过为训练有素的模型生成热图来分析模型决策，以在决策过程中吸引他们的注意力。为了关注一种可解释的人工方法，我们表明，考虑到适当的预处理步骤，研究的模型即使在训练集中存在虚假数据的情况下也可以做出无偏见的决策。我们的最佳模型具有检测准确性的94.44％，结果表明模型有利于频谱图对决策过程有利于频谱图，尤其是与韵律域有关的频谱图中的高能量区域，而F0也会导致有效的COVID-COVID-19检测。
During the outbreak of COVID-19 pandemic, several research areas joined efforts to mitigate the damages caused by SARS-CoV-2. In this paper we present an interpretability analysis of a convolutional neural network based model for COVID-19 detection in audios. We investigate which features are important for model decision process, investigating spectrograms, F0, F0 standard deviation, sex and age. Following, we analyse model decisions by generating heat maps for the trained models to capture their attention during the decision process. Focusing on a explainable Inteligence Artificial approach, we show that studied models can taken unbiased decisions even in the presence of spurious data in the training set, given the adequate preprocessing steps. Our best model has 94.44% of accuracy in detection, with results indicating that models favors spectrograms for the decision process, particularly, high energy areas in the spectrogram related to prosodic domains, while F0 also leads to efficient COVID-19 detection.
