  Topological Data Analysis (TDA) has emerged recently as a robust tool to
extract and compare the structure of datasets. TDA identifies features in data
such as connected components and holes and assigns a quantitative measure to
these features. Several studies reported that topological features extracted by
TDA tools provide unique information about the data, discover new insights, and
determine which feature is more related to the outcome. On the other hand, the
overwhelming success of deep neural networks in learning patterns and
relationships has been proven on a vast array of data applications, images in
particular. To capture the characteristics of both powerful tools, we propose
\textit{TDA-Net}, a novel ensemble network that fuses topological and deep
features for the purpose of enhancing model generalizability and accuracy. We
apply the proposed \textit{TDA-Net} to a critical application, which is the
automated detection of COVID-19 from CXR images. The experimental results
showed that the proposed network achieved excellent performance and suggests
the applicability of our method in practice.
