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

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dc.contributor.authorGoel, Garviten
dc.contributor.committeechairFeng, Wu-chunen
dc.contributor.committeememberMartin, Thomas L.en
dc.contributor.committeememberCao, Guohuaen
dc.contributor.committeememberMin, Chang Wooen
dc.contributor.committeememberHuang, Jia-Binen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2022-02-24T19:00:37Zen
dc.date.available2022-02-24T19:00:37Zen
dc.date.issued2022-02-24en
dc.description.abstractComputed 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.en
dc.description.abstractgeneralComputed tomography has been widely used in the medical diagnosis of diseases, such as cancer/tumor, viral pneumonia, and more recently, COVID-19. However, the risk of cancer associated with X-ray dose in CT scans limits the use of computed tomography in biomedical imaging. Therefore we develop a deep learning-based image enhancement algorithm that can be used with low X-ray dose computed tomography scanners to generate high-quality CT images. The algorithm uses a state-of-the-art convolution neural network for increased performance and computational efficiency. Further, we use image enhancement algorithm to develop a framework of AI-based tools to improve the accuracy of COVID-19 diagnosis. We test and validate the framework with clinical COVID-19 data. Our framework applies to the diagnosis of COVID-19 and its variants, and other diseases that can be diagnosed via computed tomography. We utilize high-performance computing techniques to reduce the execution time of training and testing AI models in our framework. We also evaluate the efficacy of training and inference of the neural network on heterogeneous computing platforms, including multi-core CPU, many-core GPU, and field-programmable gate arrays (FPGA), in terms of speed and power consumption.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:33940en
dc.identifier.urihttp://hdl.handle.net/10919/108853en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectAIen
dc.subjectbiomedical imagingen
dc.subjectcorona virusen
dc.subjectCOVID-19en
dc.subjectdeep learningen
dc.subjectdiagnosisen
dc.subjectneural networksen
dc.subjectGPUen
dc.subjectField programmable gate arraysen
dc.titleReal-Time Computed Tomography-based Medical Diagnosis Using Deep Learningen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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