“Automated Diagnosis of Parkinson’s Disease in Neuroimaging Data Using Deep Learning”

by | Mar 20, 2019

Track 4: HU Research | Room: 1302
Pranita Patil, Research Fellow and PhD Student, Harrisburg University

The true cause for Parkinson’s Disease (PD) is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Recent studies and advances in brain imaging technology have shown improvement in visual interpretation for PD. The proposed study mainly focuses on finding distinguishing characteristics in neuroimaging data which assist in diagnosing the PD with automation and higher accuracy. Since deep learning techniques have proved their ability in achieving the highest accuracy in different imaging-based applications, the main objective is to use deep learning framework to perform and improve automated imaging diagnosis for PD in clinical settings. This proposed approach successfully classified noninvasive and less expensive neuroimaging resting-state functional MRI (rs-fMRI) data to distinguish PD from the control group.

Pranita Patil is a Research Fellow and Ph.D. student at Harrisburg University of Science and Technology (HU). She earned her M.S. in electrical & computer engineering at Oklahoma State University and M.S. in Analytics at HU. After 4 years of industrial experience in deep and machine learning, Pranita returned to pursue a doctoral degree in Data Science at HU. In the past decades, she has published work notably in the deep learning field. She is currently working on different research projects such as detection of Parkinson’s disease using deep learning, SRBC signal to noise, and SRBC environmental monitoring project. She wishes to help the community through her research and knowledge.