University of Illinois Urbana-Champaign

NONALCOHOLIC FATTY LIVER DISEASE DIAGNOSIS USING CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS ON RADIOFREQUENCY SIGNAL SPECTROGRAMS

He, Churan

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FA21-ECE499Thesis-He, Churan.pdf

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https://hdl.handle.net/2142/124985

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Title
NONALCOHOLIC FATTY LIVER DISEASE DIAGNOSIS USING CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS ON RADIOFREQUENCY SIGNAL SPECTROGRAMS
Author(s)
He, Churan
Issue Date
2021-12-01
Keyword(s)
Nonalcoholic fatty liver disease (NAFLD), Ultrasound, Radiofrequency, Deep Learning, Spectrogram, Recurrent Neural Network
Date of Ingest
2024-10-29T13:11:05-05:00
Abstract
Nonalcoholic fatty liver disease (NAFLD) is increasingly common around the world, and it is the most common form of chronic liver disease in the United States. The gold standard of NAFLD diagnosis, liver biopsy, is invasive and unideal for early-stage screening. Researchers used deep learning methods of raw radiofrequency (RF) ultrasound signals to perform a non-invasive assessment of patients' liver fat fractions. Han et al. proposed a method that used one-dimensional convolutional neural networks (1D-CNN) to estimate the liver fat fraction and diagnose nonalcoholic fatty liver disease (NAFLD) using time-domain RF signals. The neural network model detects patterns in raw RF signals and finds subtle correlations between these patterns with the liver fat fraction. In our research, we propose a new machine learning approach that can take advantage of the characteristics of RF signals. Compared to Han et al.'s method that directly utilizes the time-domain RF signals, which contain the signal’s spectrum information along with time. With the spectrograms, our model can analyze how frequency components evolve from the beginning to the end of the signals using the recurrent neural network (RNN) and extract more information than the 1D-CNN model. The result demonstrates that CNN combined with RNN on the spectrogram we proposed and implemented is capable of achieving a higher Pearson correlation coefficient between the estimated fat fraction and the magnetic resonance imaging derived proton density fat fraction (MRI-PDFF) (r = 0.869) than the 1D-CNN model (r = 0.835).
Type of Resource
text
Genre of Resource
dissertation/thesis
Language
eng

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