P
Pritee Khanna
Researcher at Indian Institute of Information Technology, Design and Manufacturing, Jabalpur
Publications - 111
Citations - 1706
Pritee Khanna is an academic researcher from Indian Institute of Information Technology, Design and Manufacturing, Jabalpur. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 19, co-authored 96 publications receiving 1050 citations. Previous affiliations of Pritee Khanna include Indian Institute of Technology Kharagpur & Indian Institutes of Information Technology.
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Journal ArticleDOI
Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review
Muhammad Tanveer,Bharat Richhariya,R. U. Khan,Aamir Rashid,Pritee Khanna,Mukesh Prasad,Chin-Teng Lin +6 more
TL;DR: A large number of novel and efficient automated techniques are needed for early diagnosis of Alzheimer’s disease, and many novel approaches to diagnosis are being developed.
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Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM
Shubhi Sharma,Pritee Khanna +1 more
TL;DR: This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant, and proves the applicability of Zernike moments as a fitting texture descriptor.
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A FPGA based implementation of Sobel edge detection
TL;DR: An architecture for Sobel edge detection on Field Programmable Gate Array (FPGA) board, which is inexpensive in terms of computation and reduces the time and space complexity compare to two existing architectures.
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Random Distance Method for Generating Unimodal and Multimodal Cancelable Biometric Features
Harkeerat Kaur,Pritee Khanna +1 more
TL;DR: A novel template transformation technique named random distance method is proposed which not only generates discriminative and privacy preserving revocable pseudo-biometric identities, but also reduces their size by 50%.
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Glioma detection on brain MRIs using texture and morphological features with ensemble learning
TL;DR: The proposed non-invasive CAD system based on brain Magnetic Resonance Imaging (MRIs) is capable of assisting radiologists and clinicians to detect not only the presence, but also the type of glioma tumors.