NDL-Net: A Hybrid Deep Learning Framework for Diagnosing Neonatal Respiratory Distress Syndrome from Chest X-Rays

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.