# A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients

## TL;DR

A deep learning system using CT scans helps diagnose and assess severity of COVID-19 in lung cancer patients, improving early detection and prognosis.

## Contribution

A novel dual-module deep learning system for differentiating and severity-classifying COVID-19 pneumonia in lung cancer patients using non-enhanced CT scans.

## Key findings

- The first diagnostic module achieved 88.84% test accuracy in distinguishing COVID-19 pneumonia from other pneumonias.
- The second module achieved 91.84% test accuracy in identifying severe COVID-19 cases.
- Strong correlation was found between deep learning features and KL-6, a biomarker for lung damage.

## Abstract

Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection.

This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6.

The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6.

Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.