Biomedical Engineering and Bioengineering Commons^™

Error Prevention In Sensors And Sensor Systems, Pedro J. Chacon Dominguez

LSU Doctoral Dissertations

Achievements in all fields of engineering and fabrication methods have led towards optimization and integration of multiple sensing devices into a concise system. These advances have caused significant innovation in various commercial, industrial, and research efforts. Integrations of subsystems have important applications for sensor systems in particular. The need for reporting and real time awareness of a device’s condition and surroundings have led to sensor systems being implemented in a wide variety of fields. From environmental sensors for agriculture, to object characterization and biomedical sensing, the application for sensor systems has impacted all modern facets of innovation. With these innovations, …

Developing An Optomechanical Approach For Characterizing Mechanical Properties Of Single Adherent Cells, Ali Mehrnezhad

LSU Doctoral Dissertations

Mechanical properties of a cell reflect its biological and pathological conditions including cellular disorders and fundamental cellular processes such as cell division and differentiation. There have been active research efforts to develop high-throughput platforms to mechanically characterize single cells. Yet, many of these research efforts are focused on suspended cells and use a flow-through configuration. Therefore, adherent cells are detached prior to the characterization, which seriously perturbs the cellular conditions. Also, methods for adherent cells are limited in their throughput.

My study is aimed to fill the technical gap in the field of single cell analysis, which is a high-throughput …

Hierarchical Fusion Based Deep Learning Framework For Lung Nodule Classification, Kazim Sekeroglu

LSU Doctoral Dissertations

Lung cancer is the leading cancer type that causes the mortality in both men and women. Computer aided detection (CAD) and diagnosis systems can play a very important role for helping the physicians in cancer treatments. This dissertation proposes a CAD framework that utilizes a hierarchical fusion based deep learning model for detection of nodules from the stacks of 2D images. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest (VOI). This study explores three different …