HepNet: Deep neural network for classification of early-stage hepatic steatosis using microwave signals

Article

Hasan, Sazid, Brankovic, Aida, Awal, Md Abdul, Rezaeieh, Sasan Ahdi, Keating, Shelley E., Abbosh, Amin M. and Zamani, Ali. 2025. "HepNet: Deep neural network for classification of early-stage hepatic steatosis using microwave signals." IEEE Journal of Biomedical and Health Informatics. 29 (1), pp. 142-151. https://doi.org/10.1109/JBHI.2024.3489626
Article Title

HepNet: Deep neural network for classification of early-stage hepatic steatosis using microwave signals

ERA Journal ID13572
Article CategoryArticle
AuthorsHasan, Sazid, Brankovic, Aida, Awal, Md Abdul, Rezaeieh, Sasan Ahdi, Keating, Shelley E., Abbosh, Amin M. and Zamani, Ali
Journal TitleIEEE Journal of Biomedical and Health Informatics
Journal Citation29 (1), pp. 142-151
Number of Pages10
Year2025
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1089-7771
1558-0032
2168-2194
2168-2208
Digital Object Identifier (DOI)https://doi.org/10.1109/JBHI.2024.3489626
Web Address (URL)https://ieeexplore.ieee.org/document/10740654
Abstract

Hepatic steatosis, a key factor in chronic liver diseases, is difficult to diagnose early. This study introduces a classifier for hepatic steatosis using microwave technology, validated through clinical trials. Our method uses microwave signals and deep learning to improve detection to reliable results. It includes a pipeline with simulation data, a new deep-learning model called HepNet, and transfer learning. The simulation data, created with 3D electromagnetic tools, is used for training and evaluating the model. HepNet uses skip connections in convolutional layers and two fully connected layers for better feature extraction and generalization. Calibration and uncertainty assessments ensure the model's robustness. Our simulation achieved an F1-score of 0.91 and a confidence level of 0.97 for classifications with entropy ≤0.1, outperforming traditional models like LeNet (0.81) and ResNet (0.87). We also use transfer learning to adapt HepNet to clinical data with limited patient samples. Using 1H-MRS as the standard for two microwave liver scanners, HepNet achieved high F1-scores of 0.95 and 0.88 for 94 and 158 patient samples, respectively, showing its clinical potential.

KeywordsDeep learning; electromagnetic imaging; microwave imaging; hepatic steatosis classification; wavelet
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460201. Artificial life and complex adaptive systems
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Byline AffiliationsUniversity of Queensland
School of Engineering
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