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ORCID

https://orcid.org/0000-0002-5366-9504

Access Type

Open Access Thesis

Document Type

thesis

Degree Program

Electrical & Computer Engineering

Degree Type

Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)

Year Degree Awarded

2021

Month Degree Awarded

February

Abstract

The world is increasingly controlled by machine learning and deep learning. Deep neural networks are becoming powerful, encroaching on many tasks in computer vision system areas previously seen as the unique domain of humans, such as image classification, object detection, semantic segmentation, and instance segmentation. The success of a deep learning model at a specific application is determined by a sequence of choices, like what kind of deep neural network will be used, what data to be fed into the deep model, and what manners will be adopted to train a deep model.

The goal of this work is to design a practical, lightweight image classification model built and trained from scratch which serves as an assistant to researchers and users to recognize if a small bug is a tick. Some of the images used in this work were collected by specialists using a microscope in the Laboratory of Medical Zoology (LMZ) at the University of Massachusetts Amherst.

The following techniques are used in this work. We generated four datasets by collecting 53,150 images of small bugs and cleaning the data by deleting images with low quality. Both preprocessed images and augmented images were used in the training and validation processes. Initially, we proposed the use of five lightweight CNNs. We trained each network on the same training dataset and evaluated them using the same validation dataset. After comparing these five architectures, we chose the one with the best performance, named TickNet. We compared TickNet and five other classical image classification architectures used for large-scale image recognition tasks. We determined TickNet outperforms the five classical networks in model size, number of parameters, testing time on both a CPU and GPU with a tradeoff in testing accuracy. We deployed applications on an Android mobile phone to do binary classifications and four-class image classifications to conclude the research.

Disclaimer: This work or any part of it should not be used as guidance or instruction regarding the diagnosis, care, or treatment of tick-borne diseases or supersede existing guidance.

DOI

https://doi.org/10.7275/20410078

First Advisor

Lixin Gao

Second Advisor

Russell Tessier

Third Advisor

Jeremy Gummeson

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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