Award Date

May 2024

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Committee Member

Mingon Kang

Second Committee Member

Laxmi Gewali

Third Committee Member

Fatma Nasoz

Fourth Committee Member

Shaikh Arifuzzaman

Fifth Committee Member

Mira Han

Number of Pages

30

Abstract

The acid-fast stain is frequently used for laboratory diagnosis of tuberculosis. It is a labor intensive task requiring thorough examination of extremely high-resolution images to pinpoint the presence of the mycobacteria. This paper presents a machine learning assisted slide image analysis tool with the aim of aiding histopathology professionals in the accurate diagnosis of tuberculosis in patients through the analysis of microscopic imagery. The proposed tool combines a digital whole slide image viewer with an online learning framework. We also conducted a survey of different state-of-the-art online learning methods, and found that MIR with pre-training has the best performance on the CIFAR-10 dataset.

Keywords

Acid-Fast Staining; Histopathology; Online Learning; Tuberculosis

Disciplines

Computer Sciences

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/


Share

COinS