Physical Sciences and Mathematics Commons^™

A Deep Recurrent Neural Network With Iterative Optimization For Inverse Image Processing Applications, Masaki Ikuta

Theses and Dissertations

Many algorithms and methods have been proposed for inverse image processing applications, such as super-resolution, image de-noising, and image reconstruction, particularly with the recent surge of interest in machine learning and deep learning methods.

As for Computed Tomography (CT) image reconstruction, the most recently proposed methods are limited to image domain processing, where deep learning is used to learn the mapping between a true image data set and a noisy image data set in the image domain. While deep learning-based methods can produce higher quality images than conventional model-based algorithms, these methods have a limitation. Deep learning-based methods used in …

Prediction Of Concurrent Hypertensive Disorders In Pregnancy And Gestational Diabetes Mellitus Using Machine Learning Techniques, Mary Ejiwale

Theses and Dissertations

Gestational diabetes mellitus and hypertensive disorders in pregnancy are serious maternal health conditions with immediate and lifelong mother-child health consequences. These obstetric pathologies have been widely investigated, but mostly in silos, while studies focusing on their simultaneous occurrence rarely exist. This is especially the case in the machine learning domain. This retrospective study sought to investigate, construct, evaluate, compare, and isolate a supervised machine learning predictive model for the binary classification of co-occurring gestational diabetes mellitus and hypertensive disorders in pregnancy in a cohort of otherwise healthy pregnant women. To accomplish the stated aims, this study analyzed an extract (n=4624, …

A Machine Learning Pipeline With Switching Algorithms To Predict Lung Cancer And Identify Top Features, Anika Tasnim

Theses and Dissertations

Lung cancer is the leading cause of cancer-related death around the world. Early detection is a critical factor for its effective treatment. To facilitate early-stage prediction, a Machine Learning (ML) pipeline has been built that uses inpatient admission data to train four ML models. The data is dynamically loaded into a database, cleaned, and passed through the SelectKBest selector to identify the top features influencing the prognosis, which are then fed into the pipeline and fitted to the ML models to make the forecast. Among the models used, Decision Tree provides the highest accuracy (97.09%), followed by Random Forest (94.07%). …

Medical Image Segmentation Using Machine Learning, Masoud Khani

Theses and Dissertations

Image segmentation is the most crucial step in image processing and analysis. It can divide an image into meaningfully descriptive components or pathological structures. The result of the image division helps analyze images and classify objects. Therefore, getting the most accurate segmented image is essential, especially in medical images. Segmentation methods can be divided into three categories: manual, semiautomatic, and automatic. Manual is the most general and straightforward approach. Manual segmentation is not only time-consuming but also is imprecise. However, automatic image segmentation techniques, such as thresholding and edge detection, are not accurate in the presence of artifacts like noise …