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KD-SqueezeNet: an efficient deep learning strategy for the multi-task diagnosis of neonatal lung diseases
Jie Li1,2, Renyi Pan3, Fa Tian4
1The Department of Radiology, Second Affiliated Hospital and Yuying Childrens Hospital of Wenzhou Medical University, Xueyuan West Road, Wenzhou, 325027, China.
Background:
The integration of deep learning in medical imaging has reached proficiency levels akin to expert clinicians, particularly in tasks requiring precise image categorization.
Objective:
This study developed KD-SqueezeNet, a lightweight deep learning model, to classify neonatal lung diseases via chest radiographs, aiming to enhance diagnostic accuracy and efficiency.
Materials And Methods:
Retrospective analysis included 2,089 neonates with clinical and imaging records. Chest radiographs were categorized into five groups: bronchopulmonary dysplasia (group 1, n=205), pneumonia (group 2, n=505), pneumothorax (group 3, n=201), respiratory distress syndrome (group 4, n=629), and normal (group 5, n=549). Data were divided into training, testing, and validation sets with an 8:1:1 ratio. Performance metrics included accuracy (Ac), precision (Pr), recall (Rc), F1 score (F1), parameter count, and area under the receiver operating characteristic curve (AUROC).
Results:
KD-SqueezeNet, an interpretable model integrating knowledge distillation, outperformed EfficientNet, GhostNet, InceptionNet, RegNet, and Vision Transformer. For binary classification (healthy/diseased), it achieved Ac=0.93, Pr=0.93, Rc=0.93, F1=0.93, and AUROC=0.97 with only 723,522 parameters. In four-class classification (group 1/group 2/group 3/group 4), it attained Ac=0.86, Pr=0.86, Rc=0.86, and F1=0.86 (724,548 parameters), with class-specific AUROCs: group 1 (0.97), group 2 (0.94), group 3 (0.96), group 4 (0.97).
Conclusion:
KD-SqueezeNet not only excels in accuracy and stability but also demonstrates efficient utilization of computational resources, making it suitable for rapid diagnosis and deployment in practical applications. It holds significant clinical value in terms of saving time and server space, providing auxiliary diagnostics, supporting clinical decision-making, and improving patient outcomes in large-scale screening contexts.
