Deep Learning Models For Biomedical Data Analysis

Author

Lucy Nwosu, Prairie View A&M University

Date of Award

8-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Discipline

Electrical Engineering

Abstract

The field of biomedical data analysis is a vibrant area of research dedicated to extracting valuable insights from a wide range of biomedical data sources, including biomedical images and genomics data. The emergence of deep learning, an artificial intelligence approach, presents significant prospects for enhancing biomedical data analysis and knowledge discovery. This dissertation focused on exploring innovative deep-learning methods for biomedical image processing and gene data analysis.

During the COVID-19 pandemic, biomedical imaging data, including CT scans and chest x-rays, played a pivotal role in identifying COVID-19 cases by categorizing patient chest x-ray outcomes as COVID-19-positive or negative. While supervised deep learning methods have effectively recognized COVID-19 patterns in chest x-ray datasets, the availability of annotated training data remains limited. To address this challenge, the thesis introduced a semi-supervised deep learning model named ssResNet, built upon the Residual Neural Network (ResNet) architecture. The model combines supervised and unsupervised paths, incorporating a weighted supervised loss function to manage data imbalance. The strategies to diminish prediction uncertainty in deep learning models for critical applications like medical image processing is explore. It achieves this through an ensemble deep learning model, integrating bagging deep learning and model calibration techniques. This ensemble model not only boosts biomedical image segmentation accuracy but also reduces prediction uncertainty, as validated on a comprehensive chest x-ray image segmentation dataset.

Furthermore, the thesis introduced an ensemble model integrating "Proformer" and ensemble learning methodologies. This model constructs multiple independent "Proformers" for predicting gene expression, their predictions are combined through weighted averaging to generate final predictions. Experimental outcomes underscore the efficacy of this ensemble model in enhancing prediction performance across various metrics.

In conclusion, this dissertation advances biomedical data analysis by harnessing the potential of deep learning techniques. It devises innovative approaches for processing biomedical images and gene data. By leveraging deep learning's capabilities, this work paves the way for further progress in biomedical data analytics and its applications within clinical contexts.

Index Terms- biomedical data analysis, COVID-19, deep learning, ensemble learning, gene data analytics, medical image segmentation, prediction uncertainty, Proformer, Residual Neural Network (ResNet), semi-supervised learning.

Committee Chair/Advisor

Xishuang Dong

Committee Co-Chair:

Lijun Qian

Committee Member

Xiangfang Li

Committee Member

Seungchan Kim

Committee Member

Noushin Ghaffarti

Publisher

Prairie View A&M University

Rights

Date of Digitization

10/04/2023

Contributing Institution

John B Coleman Library

City of Publication

Prairie View

MIME Type

Application/PDF

Recommended Citation

Nwosu, L. (2023). Deep Learning Models For Biomedical Data Analysis. Retrieved from https://digitalcommons.pvamu.edu/pvamu-dissertations/29