Chiranji Lal Chowdhary

Work place: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India



Research Interests: Computer systems and computational processes, Computer Vision, Image Compression, Image Manipulation, Image Processing


Chiranji Lal Chowdhary, PhD, is Associate Professor in the School of Information Technology and Engineering at the Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India, where he has been since 2010. From 2006 to 2010 he worked at M.S. Ramaiah Institute of Technology in Bangalore, eventually as a lecturer. His research interests span both computer vision and image processing. Much of his work has been on images, mainly through the application of image processing, computer vision, pattern recognition, machine learning, biometric systems, deep learning, soft computing, and computational intelligence. He has given few invited talks on medical image processing. Professor Chowdhary is editor/co-editor of more than seven books and is the author of over 50 research articles on computer science. He filed two Indian patents deriving from his research.


Author Articles
Non-invasive Detection of Parkinson's Disease Using Deep Learning

By Chiranji Lal Chowdhary R. Srivatsan

DOI:, Pub. Date: 8 Apr. 2022

Being a near end to a confident life, there is no simple test to diagnose stages of patients with Parkinson's disease (PD) for a patient. In order to estimate whether the disease is in control and to check if medications are regulated, the stage of the disease must be able to be determined at each point. Clinical techniques like the specific single-photon emission computerized tomography (SPECT) scan called a dopamine transporter (DAT) scan is expensive to perform regularly and may limit the patient from getting regular progress of his body. The proposed approach is a lightweight computer vision method to simplify the detection of PD from spirals drawn by the patients. The customized architecture of convolutional neural network (CNN) and the histogram of oriented gradients (HoG) based feature extraction. This can progressively aid early detection of the disease provisioning to improve the future quality of life despite the threatening symptoms by ensuring that the right medication dosages are administered in time. The proposed lightweight model can be readily deployed on embedded and hand-held devices and can be made available to patients for a quick self-examination.

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