Real-Time Computed Tomography-based Medical Diagnosis Using Deep Learning

Computed tomography has been widely used in medical diagnosis to generate accurate images of the body's internal organs. However, cancer risk is associated with high X-ray dose CT scans, limiting its applicability in medical diagnosis and telemedicine applications. CT scans acquired at low X-ray dose generate low-quality images with noise and streaking artifacts. Therefore we develop a deep learning-based CT image enhancement algorithm for improving the quality of low-dose CT images. Our algorithm uses a convolution neural network called DenseNet and Deconvolution network (DDnet) to remove noise and artifacts from the input image. To evaluate its advantages in medical diagnosis, we use DDnet to enhance chest CT scans of COVID-19 patients. We show that image enhancement can improve the accuracy of COVID-19 diagnosis (~5% improvement), using a framework consisting of AI-based tools. For training and inference of the image enhancement AI model, we use heterogeneous computing platform for accelerating the execution and decreasing the turnaround time. Specifically, we use multiple GPUs in distributed setup to exploit batch-level parallelism during training. We achieve approximately 7x speedup with 8 GPUs running in parallel compared to training DDnet on a single GPU. For inference, we implement DDnet using OpenCL and evaluate its performance on multi-core CPU, many-core GPU, and FPGA. Our OpenCL implementation is at least 2x faster than analogous PyTorch implementation on each platform and achieves comparable performance between CPU and FPGA, while FPGA operated at a much lower frequency.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Goel, Garvit

Chair dc:contributor.committeechair

• Feng, Wu-chun

Committee members dc:contributor.committeemember

• Martin, Thomas L.
• Cao, Guohua
• Min, Chang Woo
• Huang, Jia-Bin

• AI
• biomedical imaging
• corona virus
• COVID-19
• deep learning
• diagnosis
• neural networks
• GPU
• Field programmable gate arrays

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:33940

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/108853