Rule-based deep learning method for prognosis of neonatal hypoxic-ischaemic encephalopathy by using susceptibility weighted image analysis

Abstract

Susceptibility weighted imaging (SWI) of neonatal hypoxic-ischaemic brain injury can provide assistance in the prognosis of neonatal hypoxic-ischaemic encephalopathy (HIE). We propose a convolutional neural network model to classify SWI images with HIE. Due to the lack of a large dataset, transfer learning method with fine-tuning a pre-trained ResNet 50 is introduced. We randomly select 11 datasets from patients with normal neurology outcomes (n = 31) and patients with abnormal neurology outcomes (n = 11) at 24 months of age to avoid bias in classification due to any imbalance in the data. Then we develop a rule-based system to improve the classification performance, with an accuracy of 0.93 ± 0.09. We also compute heatmaps produced by the Grad-CAM technique to analyze which areas of SWI images contributed more to the classification patients with abnormal neurology outcome, such regions can interpret the relationship between the brain regions affected by hypoxic-ischaemic and neurodevelopmental outcomes of infants with HIE at the age of 2 years.

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