A network for acute bilirubin encephalopathy classification based upon attention mechanism and 3D convolution kernels

Haoyu Zhang; Zhaohui Wang

doi:10.1117/12.2679997

27 June 2023 A network for acute bilirubin encephalopathy classification based upon attention mechanism and 3D convolution kernels

Haoyu Zhang, Zhaohui Wang

Author Affiliations +

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 1270518 (2023) https://doi.org/10.1117/12.2679997
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Neonatal hyperbilirubinemia is a disease of bilirubin metabolism disorder, which is a common in newborns. Without timely medical attention, neonatal hyperbilirubinemia may develop into acute bilirubin encephalopathy, resulting in serious long-term neurological deficits. Magnetic resonance imaging, as a non-invasive imaging technique, is widely used in the diagnosis of acute bilirubin encephalopathy in newborns. However, the T1-weighted images of magnetic resonance imaging of newborns with normal myelin development and newborns with acute bilirubin encephalopathy have similar high signal intensity, making it difficult to make a clinical diagnosis based on the conventional radiological reading. As an important computer-aided diagnosis method, deep convolutional neural network has been widely used to improve the work efficiency of radiologists. In this paper, a convolutional neural network based on classification network for acute bilirubin encephalopathy is proposed. It contains a feature fusion section and a fairly deep Resnet classification network. Experimental results show that the performance of the proposal is better than those of deep learning models in discussion.

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Haoyu Zhang and Zhaohui Wang "A network for acute bilirubin encephalopathy classification based upon attention mechanism and 3D convolution kernels", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 1270518 (27 June 2023); https://doi.org/10.1117/12.2679997

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KEYWORDS

Convolution

Feature fusion

Magnetic resonance imaging

3D modeling

Biological samples

Blood

Education and training

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