DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19

TL;DR

This paper introduces DuCN, a dual-children deep learning network that enhances COVID-19 detection and case recommendation by combining lung segmentation, disease diagnosis, and similar case retrieval, improving accuracy.

Contribution

The study presents a novel dual-children network based on ResNet-18 with triplet loss and distance maps for improved COVID-19 diagnosis and case recommendation.

Findings

Achieves high diagnostic accuracy on CC-CCII dataset

Outperforms existing COVID-19 detection methods

Provides effective similar case recommendations for clinicians

Abstract

Early detection of the coronavirus disease 2019 (COVID-19) helps to treat patients timely and increase the cure rate, thus further suppressing the spread of the disease. In this study, we propose a novel deep learning based detection and similar case recommendation network to help control the epidemic. Our proposed network contains two stages: the first one is a lung region segmentation step and is used to exclude irrelevant factors, and the second is a detection and recommendation stage. Under this framework, in the second stage, we develop a dual-children network (DuCN) based on a pre-trained ResNet-18 to simultaneously realize the disease diagnosis and similar case recommendation. Besides, we employ triplet loss and intrapulmonary distance maps to assist the detection, which helps incorporate tiny differences between two images and is conducive to improving the diagnostic accuracy. For each confirmed COVID-19 case, we give similar cases to provide radiologists with diagnosis and treatment references. We conduct experiments on a large publicly available dataset (CC-CCII) and compare the proposed model with state-of-the-art COVID-19 detection methods. The results show that our proposed model achieves a promising clinical performance.