N
Neeraj Sharma
Researcher at Indian Institute of Technology (BHU) Varanasi
Publications - 183
Citations - 2057
Neeraj Sharma is an academic researcher from Indian Institute of Technology (BHU) Varanasi. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 14, co-authored 134 publications receiving 1349 citations. Previous affiliations of Neeraj Sharma include Indian Institutes of Technology & Institute of Medical Sciences, Banaras Hindu University.
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Automated medical image segmentation techniques
TL;DR: This review provides details of automated segmentation methods, specifically discussed in the context of CT and MR images, and the relative merits and limitations of methods currently available for segmentation of medical images.
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Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network
Neeraj Sharma,Amit Kumar Ray,Shiru Sharma,Kaushal K. Shukla,Satyajit Pradhan,Lalit Mohan Aggarwal +5 more
TL;DR: An attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features with ANN as segmentation and classifier tool, which combines second, third, and fourth steps into one algorithm.
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Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review.
TL;DR: In this article, the authors provided the first narrative deep learning review by considering all facets of image classification using AI and employed a PRISMA search strategy using Google Scholar, PubMed, IEEE, and Elsevier Science Direct, through which 127 relevant HDL studies were considered.
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Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound
Pankaj K. Jain,Neeraj Sharma,Argiris A. Giannopoulos,Luca Saba,Andrew Nicolaides,Jasjit S. Suri +5 more
TL;DR: In this paper, the authors used solo deep learning and hybrid deep learning (HDL) models for plaque segmentation in the internal carotid artery (ICA) using B-mode ultrasound (US).
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Bat optimization based neuron model of stochastic resonance for the enhancement of MR images
TL;DR: The proposed modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging performs well and has been found helpful in the better diagnosis of MR images.