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TickNet: A Lightweight Deep Classifier for Tick Recognition

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dc.contributor.advisorLixin Gao
dc.contributor.advisorRussell Tessier
dc.contributor.advisorJeremy Gummeson
dc.contributor.authorWang, Li
dc.contributor.departmentUniversity of Massachusetts Amherst
dc.contributor.departmentElectrical & Computer Engineering
dc.date2024-03-28T19:30:08.000
dc.date.accessioned2024-04-26T18:06:45Z
dc.date.available2024-04-26T18:06:45Z
dc.date.issued2021-02-01
dc.date.submittedFebruary
dc.date.submitted2021
dc.description.abstractThe 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.
dc.description.degreeMaster of Science in Electrical and Computer Engineering (M.S.E.C.E.)
dc.identifier.doihttps://doi.org/10.7275/20410078
dc.identifier.orcidhttps://orcid.org/0000-0002-5366-9504
dc.identifier.urihttps://hdl.handle.net/20.500.14394/32686
dc.relation.urlhttps://scholarworks.umass.edu/cgi/viewcontent.cgi?article=2063&context=masters_theses_2&unstamped=1
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.source.statuspublished
dc.subjectDeep Neural Network
dc.subjectImage Classification
dc.subjectTick Recognition
dc.subjectOther Computer Engineering
dc.subjectRobotics
dc.titleTickNet: A Lightweight Deep Classifier for Tick Recognition
dc.typeopenaccess
dc.typearticle
dc.typethesis
digcom.contributor.authorisAuthorOfPublication|email:liw@umass.edu|institution:University of Massachusetts Amherst|Wang, Li
digcom.identifiermasters_theses_2/1029
digcom.identifier.contextkey20410078
digcom.identifier.submissionpathmasters_theses_2/1029
dspace.entity.typePublication
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