Honors Theses

Date of Award

Spring 5-1-2021

Document Type

Undergraduate Thesis


Computer and Information Science

First Advisor

Dawn Wilkins

Second Advisor

Ana Pavel

Third Advisor

Yixin Chen

Relational Format



Machine learning and image processing techniques have been widely implemented in the field of medicine to help accurately diagnose a multitude of medical conditions. The automated diagnosis of skin melanoma is one such instance. However, a majority of the successful machine learning models that have been implemented in the past have used deep learning approaches where only raw image data has been utilized to train machine learning models, such as neural networks. While they have been quite effective at predicting the condition of these lesions, they lack key information about the images, such as clinical data, and features that medical professionals consistently rely on for diagnosis. This research project will explore methods to enhance machine learning models with three drastically different skin melanoma datasets, each with their own set of unique challenges. Various preprocessing techniques, machine learning models, and feature extraction methods will be compared to determine the most optimal approach for each dataset. In addition, time and space complexities of the approaches will also be analyzed in order to minimize resource consumption without causing major performance degradation to the models

Creative Commons License

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



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.