Award Date

May 2023

Degree Type


Degree Name

Master of Science in Engineering (MSE)


Electrical and Computer Engineering

First Committee Member

Brendan Morris

Second Committee Member

Sarah Harris

Third Committee Member

Shengjie Zhai

Fourth Committee Member

Mingon Kang

Number of Pages



In cooperation with the UNLV School of Medicine, this thesis is part of a bigger project aimed at developing a post-op surgery device for the constant unattended monitoring of a patient’s tissue. This device is intended to monitor areas of surgical sites and detect any complications without the need for continuous supervision by medical professionals. One such complication includes inadequate tissue perfusion, or a lack of blood flow reaching a patient’s tissue. The role of this thesis is to fit a convolutional neural network model to the available data gathered from voluntary participants and attempt to classify the data as a different stage of tissue perfusion in the hand area. The device is equipped with a camera setup using three sensing modalities, RGB, thermal infrared, and laser speckle contrast imaging, revealing information that a single conventional camera could not. Once the data is prepared for model training, various techniques will be explored due to the multiple sensing modalities involved in data collection. The techniques range from the training of a single image modality to the topic of data fusion, a key component of the thesis. In the early fusion implementation, the data to be fused is the image channels coming from the 3 modalities, creating a 4 to 5 channel image. The late fusion implementation will train each modality separately, and fuse feature vectors at the end of the model. This thesis will present the implementation and results of both techniques.



Degree Grantor

University of Nevada, Las Vegas




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