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Full-Text Articles in Data Science
Image Features For Tuberculosis Classification In Digital Chest Radiographs, Brian Hooper
Image Features For Tuberculosis Classification In Digital Chest Radiographs, Brian Hooper
All Master's Theses
Tuberculosis (TB) is a respiratory disease which affects millions of people each year, accounting for the tenth leading cause of death worldwide, and is especially prevalent in underdeveloped regions where access to adequate medical care may be limited. Analysis of digital chest radiographs (CXRs) is a common and inexpensive method for the diagnosis of TB; however, a trained radiologist is required to interpret the results, and is subject to human error. Computer-Aided Detection (CAD) systems are a promising machine-learning based solution to automate the diagnosis of TB from CXR images. As the dimensionality of a high-resolution CXR image is very …
Automated Morgan Keenan Classification Of Observed Stellar Spectra Collected By The Sloan Digital Sky Survey Using A Single Classifier, Michael J. Brice, Răzvan Andonie
Automated Morgan Keenan Classification Of Observed Stellar Spectra Collected By The Sloan Digital Sky Survey Using A Single Classifier, Michael J. Brice, Răzvan Andonie
All Faculty Scholarship for the College of the Sciences
The classification of stellar spectra is a fundamental task in stellar astrophysics. Stellar spectra from the Sloan Digital Sky Survey are applied to standard classification methods, k-nearest neighbors and random forest, to automatically classify the spectra. Stellar spectra are high dimensional data and the dimensionality is reduced using astronomical knowledge because classifiers work in low dimensional space. These methods are utilized to classify the stellar spectra into a complete Morgan Keenan classification (spectral and luminosity) using a single classifier. The motion of stars (radial velocity) causes machine-learning complications through the feature matrix when classifying stellar spectra. Due to the nature …