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Articles 1 - 3 of 3
Full-Text Articles in Biomedical Informatics
Hyperglycemia Detection From Sigle - Lead Ecg Using A Hybrid Cnn & Transformer Model, Adam Ayomikun Ogunjembola
Hyperglycemia Detection From Sigle - Lead Ecg Using A Hybrid Cnn & Transformer Model, Adam Ayomikun Ogunjembola
Graduate Theses, Dissertations, and Problem Reports (ETD)
Abstract
Hyperglycemia Detection from Single-Lead ECG using a Hybrid CNN & Transformer Model
Adam Ogunjembola
Diabetes Mellitus is known as high blood glucose. This high blood glucose level happens when the body has a problem with producing or using insulin. Insulin is a very important hormone that the pancreas makes to control how much glucose gets into the bloodstream and cells. Diabetes Mellitus has an effect on the body if it is not treated, such as damaging the blood vessels and nerves which can lead to stroke, kidney failure, heart attack and permanent loss of vision. Since people with diabetes …
Efficient Classification Of Very High Resolution Images, Mohammad I. Nouyed
Efficient Classification Of Very High Resolution Images, Mohammad I. Nouyed
Graduate Theses, Dissertations, and Problem Reports (ETD)
In recent decades, deep learning approaches have shown significant improvement in various image understanding tasks. However, analysis of high-resolution images remains a major challenge. In this work, we address the challenge of very high-resolution histopathological image (VHRHI) classification using a new information-theoretic discriminative patch selection approach. We show results on a high-resolution image dataset, namely, gigapixel whole slide tissue images for cancer tumors. Then we address how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. These ``weak labels'' are applied throughout the image but describe tumor regions of variable sizes and …
Machine Learning And Rna Bioinformatics, Jason Rafe Miller
Machine Learning And Rna Bioinformatics, Jason Rafe Miller
Graduate Theses, Dissertations, and Problem Reports (ETD)
The applied science of bioinformatics encompasses computational analysis of molecular biology data. Advances in genomics and DNA sequencing technology have enabled computational analysis of ribonucleic acids (RNAs), which play diverse and critical roles in most cells. To assist the study of human RNA, we trained machine learning models on RNA nucleotide sequences, devoid of domain knowledge. We built models that distinguish long non-coding lncRNA from protein-coding mRNA, and models that predict the cytoplasmic vs. nuclear preferences of lncRNAs. In a review of published lncRNA subcellular localization classifiers, we show that the commonly used validation protocol generates optimistic performance measures, and …