T
Tejaswi N. Rao
Researcher at Manipal University
Publications - 6
Citations - 85
Tejaswi N. Rao is an academic researcher from Manipal University. The author has contributed to research in topics: Medicine & Autoencoder. The author has an hindex of 2, co-authored 4 publications receiving 47 citations.
Papers
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Journal ArticleDOI
A Two Layer Sparse Autoencoder for Glaucoma Identification with Fundus Images
U. Raghavendra,Anjan Gudigar,Sulatha V. Bhandary,Tejaswi N. Rao,Edward J. Ciaccio,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +7 more
TL;DR: This study presents a CAD tool for the precise detection of glaucoma using a machine learning approach and is suggestive of its possible utility as an additional tool for verification of clinical decisions.
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Computer-aided diagnosis for the identification of breast cancer using thermogram images: A comprehensive review
U. Raghavendra,Anjan Gudigar,Tejaswi N. Rao,Edward J. Ciaccio,Eddie Y. K. Ng,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +7 more
TL;DR: The quantitative and qualitative performances of machine learning based approaches, which include segmentation based and feature extraction based methods, dimensionality reduction, and various classification schemes, as proposed in the literature are explored.
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Feature-versus deep learning-based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study
U. Raghavendra,Anjan Gudigar,Tejaswi N. Rao,Venkatesan Rajinikanth,Edward J. Ciaccio,Chai Hong Yeong,Suresh Chandra Satapathy,Filippo Molinari,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +10 more
TL;DR: A machine learning‐based handcrafted model uses quantitative techniques of enhanced elongated quinary patterns and entropies analysis and a non‐handcrafted deep learning model using Visual Geometry Group‐16 architecture for segregating GBM and LGG subjects results in 94.25% accuracy using k‐nearest neighbor classifier.
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FFCAEs: An efficient feature fusion framework using cascaded autoencoders for the identification of gliomas
Anjan Gudigar,U. Raghavendra,Tejaswi N. Rao,Jyothi Samanth,Venkatesan Rajinikanth,Suresh Chandra Satapathy,Edward J. Ciaccio,Chan Wai-Yee,U. Rajendra Acharya +8 more
TL;DR: In this paper , a feature fusion algorithm with cascaded autoencoders (CAEs), referred to as FFCAEs, was proposed to detect Glioblastoma (GBM).
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Cortical unihemispheric brain edema (CUBE) due to a multi-system inflammatory syndrome in adults (MIS-A).
TL;DR: A case of CUBE occurring along with a post COVID multi-system inflammatory syndrome in adults (MIS-A) is described; a 30-year-old man was admitted with status epilepticus and found to have CUBe and features of MIS-A.