Date of Award
8-2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Bioengineering
Committee Chair/Advisor
Dr. Bruce Z. Gao
Committee Member
Dr. David Karig
Committee Member
Dr. Lucas Schmidt
Committee Member
Dr. Tong Ye
Abstract
Hyperspectral imaging is a non-invasive imaging method capable of collecting both spatial and spectral information. However, because of the large volume of data collected, much of it is redundant or not useful for classification. Deep learning is a subset of machine learning that uses artificial neurons in a multilayered structure to learn representations from data. One of the main advantages of deep learning is the powerful feature extraction capabilities, which allow the model to learn both high- and low-level features. Convolutional neural networks are a type of deep learning model that have alternating convolutional and pooling layers capable of extracting features while also downsampling the input image to remove unimportant features, retain important features and reduce the computational load of the deep learning model.
CNNs can be combined with hyperspectral imaging to rapidly process the large amounts of data while removing unnecessary features. Thus, CNNs can be used to classify different hyperspectral images of bacteria and determine which bacteria are present in the image based on their spectral responses. From the output of the network, false color images can be generated from the input hyperspectral images to enhance visualization and provide spatial information.
In this thesis, we explained the goal of this project to design a deep learning model capable of processing hyperspectral images of bacteria and outputting classifications with high accuracy. We described a detailed procedure of the development of a three-layer convolutional neural network capable of generating predictions with approximately 96% accuracy and producing false color images of the hyperspectral inputs.
Recommended Citation
Vogelsberg, Bruce S. Jr, "Hyperspectral Image Classification of Bacteria Using a Deep Convolutional Neural Network" (2024). All Theses. 4355.
https://open.clemson.edu/all_theses/4355