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

TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Images