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Articles 1 - 5 of 5
Full-Text Articles in Computer Engineering
Radiomic Features To Predict Overall Survival Time For Patients With Glioblastoma Brain Tumors Based On Machine Learning And Deep Learning Methods, Lina Chato
UNLV Theses, Dissertations, Professional Papers, and Capstones
Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for …
The Affective Perceptual Model: Enhancing Communication Quality For Persons With Pimd, Jadin Tredup
The Affective Perceptual Model: Enhancing Communication Quality For Persons With Pimd, Jadin Tredup
UNLV Theses, Dissertations, Professional Papers, and Capstones
Methods for prolonged compassionate care for persons with Profound Intellectual and Multiple Disabilities (PIMD) require a rotating cast of import people in the subjects life in order to facilitate interaction with the external environment. As subjects continue to age, dependency on these people increases with complexity of communications while the quality of communication decreases. It is theorized that a machine learning (ML) system could replicate the attuning process and replace these people to promote independence. This thesis extends this idea to develop a conceptual and formal model and system prototype.
The main contributions of this thesis are: (1) proposal of …
An Emg-Based Patient Monitoring System Using Zynq Soc Device, Farhad Fallahlalehzari
An Emg-Based Patient Monitoring System Using Zynq Soc Device, Farhad Fallahlalehzari
UNLV Theses, Dissertations, Professional Papers, and Capstones
This thesis describes the design, development, and testing of an EMG-based patient monitoring system using the Zynq device. Zynq is a system on chip device designed by Xilinx which consists of an ARM dual cortex-A9 processor as well as an FPGA integrated into one chip. This work also analyzes the performance of image-processing algorithms on this system and compares that performance to more traditional PC-based systems. Image processing algorithms, such as Sobel edge detection, dilation and erosion, could be used in conjunction with a camera for the patient monitoring purposes. These algorithms often perform sub-optimally on processors because of their …
A Refreshable And Portable E-Braille System For The Blind And Visually Impaired, Mohammad Saadeh, Mohamed Trabia
A Refreshable And Portable E-Braille System For The Blind And Visually Impaired, Mohammad Saadeh, Mohamed Trabia
College of Engineering: Graduate Celebration Programs
- Braille is a communication system to assist the blind and visually impaired.
- Present an approach to measure fingertip forces while identifying Braille characters.
- Implement a force sensory feedback in the device to measure the force developed on the fingertip.
- Introduce a preliminary design for the device.
- Build a prototype for the device and evaluate its functionality and integrate its components
Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj
Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj
Electrical & Computer Engineering Faculty Research
The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are …