CIS Thesis Defense by Melanie Thibodeau
Title: Optimizing Datasets for Lyme Disease Detection Advisor: Iren Valova PhD, Associate Dean - College of Engineering-Professor, Computer & Information Science - University of Massachusetts Dartmouth Committee: Gokhan Kul PhD, Computer & Information Science - University of Massachusetts Dartmouth Firas Khatib Ph.D, Computer & Information Science - University of Massachusetts Dartmouth Date: Jan 17, 2025 Time: 1pm Location: Zoom https://umassd.zoom.us/j/98403102776?pwd=VKmd3RikQZbqdTkhOaIhoJdyXQE91k.1 Abstract: This thesis focuses on optimizing image datasets through augmentation methods for the detection of Lyme disease. Lyme disease often is accompanied by an erythema migrans rash, but other sorts of rashes could look similar to it. Using a public crowdsourced dataset, the object is to improve the accuracy of YoloV7 through image enhancements and augmentations. The study utilizes a combination of data preprocessing techniques, including CLAHE, photometric deformation, elastic deformation, and mixup to improve image quality and address dataset imbalances. YoloV7, an object detection model was trained on the enhanced dataset to accurately differentiate Lyme-related rashes from other dermatological conditions. The results favored the CLAHE result over the others. This work contributes to the development of more reliable, automated diagnostic tools for individual user. For further information contact Dr. Iren Valova at ivalova@umassd.edu
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Computer and Information Science Department
508.999.6692
fkhatib@umassd.edu