A Deep Network Incorporating Depthwise Separable Convolutions for Pathological Diagnosis of Chest X‐Ray Images: A Model Development and Validation Study

We propose a new network for chest X-ray pathology detection, and experimental results show that it can achieve better performance on public datasets.

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Publication Details

Journal
Health Science Reports
Published
2025-12-25
DOI
https://doi.org/10.1002/hsr2.71647
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.00

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A Deep Network Incorporating Depthwise Separable Convolutions for Pathological Diagnosis of Chest X‐Ray Images: A Model Development and Validation Study

Guanghong Deng, Wenlong Jing, Y. Li, Li Li et al.
Health Science Reports
COVID-19 diagnosis using AI
article

A Deep Network Incorporating Depthwise Separable Convolutions for Pathological Diagnosis of Chest X‐Ray Images: A Model Development and Validation Study

Guanghong Deng, Wenlong Jing, Y. Li, Li Li, Na Zhang
article en

Abstract

We propose a new network for chest X-ray pathology detection, and experimental results show that it can achieve better performance on public datasets.

Health Science ReportsVol. 9(1)
Sun Yat-sen University (CN), Sun Yat-sen Memorial Hospital (CN), Guangzhou Institute of Geography (CN), Guangzhou Electronic Technology (China) (CN)
National Natural Science Foundation of China, Guangdong Academy of Sciences, Science and Technology Planning Project of Guangdong Province
Openalex Percentile: Top 31%
COVID-19 diagnosis using AI
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