Machine learning models for screening and diagnosis of infections

Millions of people around the globe die or are severely burdened every year at the hands of infections. These infections can occur in wounds on the surface of the body, often after surgery. They also occur inside the body as a result of hazardous contact with infectious pathogens. Many of the victims of infections reside in developing countries and have little access to proper diagnostic resources. As a result, a large portion of these infection victims go without diagnosis until the effects of the infection are severely life-threatening. My research group has focused on developing tools to aid in disease screening for patients in developing areas over the past seven years. For this thesis project, I developed a Logistic Regression model that screens for infections in surgical site wounds using features extracted from visible light images of the wounds. The extracted features convey information about the texture and color of the wound in the LAB color space.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Olubeko, Olasubomi O.

Advisor dc:contributor.advisor

• Richard R. Fletcher.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123039

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123039