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Article Abstract
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). 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. NDL-Net represents a significant advancement in medical diagnostics, improving neonatal care through early detection and management of NRDS.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12251096 | PMC |
| http://dx.doi.org/10.1109/OJEMB.2025.3548613 | DOI Listing |
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