NDL-Net: a hybrid deep learning framework for diagnosing neonatal respiratory distress syndrome from chest X-rays

Arslan, MM, Yang, X, Zhao, N, Guan, L, Cui, T and Haider, D ORCID: https://orcid.org/0000-0002-9302-871X, 2025. NDL-Net: a hybrid deep learning framework for diagnosing neonatal respiratory distress syndrome from chest X-rays. IEEE Open Journal of Engineering in Medicine and Biology. ISSN 2644-1276

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Abstract

Objective: Neonatal Respiratory Distress Syndrome (NRDS) poses a significant threat to newborn health, necessitating timely and accurate diagnosis. This study introduces NDL-Net, an innovative hybrid deep learning framework designed to diagnose NRDS from chest X-rays (CXR).

Results: The architecture combines MobileNetV3 Large for efficient image processing and ResNet50 for detecting complex patterns essential for NRDS identification. Additionally, a Long Short-Term Memory (LSTM) layer analyzes temporal variations in imaging data, enhancing predictive accuracy. Extensive evaluation on neonatal CXR datasets demonstrated NDL-Net's high diagnostic performance, achieving 98.09% accuracy, 97.45% precision, 98.73% sensitivity, 98.08% F1-score, and 98.73% specificity. The model's low false negative and false positive rates underscore its superior diagnostic capabilities.

Conclusion: NDL-Net represents a significant advancement in medical diagnostics, improving neonatal care through early detection and management of NRDS.

Item Type:	Journal article
Publication Title:	IEEE Open Journal of Engineering in Medicine and Biology
Creators:	Arslan, M.M., Yang, X., Zhao, N., Guan, L., Cui, T. and Haider, D.
Publisher:	Institute of Electrical and Electronics Engineers
Date:	5 March 2025
ISSN:	2644-1276
Identifiers:	Number Type 10.1109/ojemb.2025.3548613 DOI 2425440 Other
Rights:	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	10 Apr 2025 08:22
Last Modified:	10 Apr 2025 08:22
URI:	https://irep.ntu.ac.uk/id/eprint/53393

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