Physical Sciences and Mathematics Commons^™

Image-Based Cancer Diagnosis Using Novel Deep Neural Networks, Hosein Barzekar

Theses and Dissertations

Cancer is the major cause of death in many nations. This serious illness can only be effectivelytreated if it is diagnosed early. In contrast, biomedical imaging presents challenges to both clinical institutions and researchers. Physiological anomalies are often characterized by modest modifications in individual cells or tissues, making them difficult to detect visually. Physiological anomalies are often characterized by slight abnormalities in individual cells or tissues, making them difficult to detect visually. Traditionally, anomalies are diagnosed by radiologists and pathologists with extensive training. This procedure, however, demands the participation of professionals and incurs a substantial expense, making the classification of …

Novel Deep Neural Network For Medical Image Classification, Dm Anisuzzaman

Theses and Dissertations

Medical image classification is an essential part of diagnosis, which with automation may benefit both physicians and patients in terms of time and cost. For automation, different Artificial intelligence (AI) methods, including Machine Learning (ML) and Deep Learning (DL), are used widely. Specifically, DL algorithms have become popular in classifying medical images due to their propensity for good performance. This thesis studies medical image classification problems using deep learning models. Four specific medical applications are considered: (1) Osteosarcoma cancer classification in histological images, (2) Burn wound classification, (3) Wound severity classification from clinical images, and (4) Wound type classification using …

Terrestrial Perspective On The Formation, Evolution, And Detection Of Zeolites In Lacustrine Environments On Early Mars, Gayantha Roshana Loku Kodikara

Theses and Dissertations

This study evaluates the possible formation and evolution mechanisms of zeolites on early Mars with possible explanations for their limited detections using Earth analogs. This study focuses on the formation of zeolites in the closed basin lakes where the largest relatively pure concentrations of natural zeolites are found on Earth. Five working hypotheses were formulated to explore the limited detection of zeolites in closed basin lakes on Mars and different styles of scientific reasoning with suitable examples were used to test the independent, converging lines of inquiry. Zeolites may not be identifiable in certain locations on Mars using orbital data …

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 …

Deep Learning-Based Object Detection In Wound Images, Yash Patel

Theses and Dissertations

Developing a deep neural network for wound localization was the first step towards an efficient and fully automated wound healing system. A wound localizer was developed in this research using the YOLOv3 model, and an iOS mobile app was also created with the developed localization algorithm. The developed system can detect the wound and its surrounding tissue and isolate the portion of the localized wound for future care. This will support the segmentation and classification of wound by eliminating a lot of redundant details from photos of wound. A lighter variant of YOLOv3 called tiny-YOLOv3 is used for mobile device …