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

TL;DR

TDA-Net combines topological data analysis and deep learning to improve COVID-19 detection accuracy from chest X-ray images, demonstrating promising results in clinical application.

Contribution

The paper introduces TDA-Net, a novel ensemble model that fuses topological and deep features for enhanced COVID-19 detection in chest X-rays.

Findings

Achieved high accuracy in COVID-19 detection

Demonstrated the effectiveness of topological features

Validated the model's practical applicability

Abstract

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.