U. Raghavendra

4.4k citations

84 papers · 3.1k indexed · 3 hit papers · h-index 31

Health Informatics top 1%
Radiology, Nuclear Medicine and Imaging top 1%
- Retinal Imaging and Analysis 10
- COVID-19 diagnosis using AI 6
Neurology top 2%
- Brain Tumor Detection and Classification 15
- Intracerebral and Subarachnoid Hemorrhage Research 6
Ophthalmology top 1%
Computer Vision and Pattern Recognition top 1%
- Advanced Neural Network Applications 6
- Digital Imaging for Blood Diseases 5
Artificial Intelligence
- AI in cancer detection 10
Epidemiology
- Acute Ischemic Stroke Management 7

Co-authors: U. Rajendra Acharya Anjan Gudigar Jen Hong Tan Hamido Fujita Yuki Hagiwara Sulatha V. Bhandary Shu Lih Oh Edward J. Ciaccio
Cited by: Health Informatics Radiology, Nuclear Medicine and Imaging Neurology
Journals: IEEE Access (8 papers)Sensors (2 papers)Information Sciences (1 paper)
Partner nations: India Singapore Malaysia

In The Last Decade

U. Raghavendra

80 papers receiving 3.0k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

2021 Journal of Applied Biomedicine
2018 Information Sciences
2018 Neural Computing and Applications

Peers

Countries citing papers authored by U. Raghavendra

Since Specialization

Citations

This map shows the geographic impact of U. Raghavendra's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by U. Raghavendra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites U. Raghavendra more than expected).

Fields of papers citing papers by U. Raghavendra

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by U. Raghavendra. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by U. Raghavendra. The network helps show where U. Raghavendra may publish in the future.

Co-authorship network

The 25 scholars most cited alongside U. Raghavendra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with U. Raghavendra Line = papers co-authored together U. Raghavendra links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

#	Work
1	A Dual-Stream Deep Learning Architecture With Adaptive Random Vector Functional Link for Multi-Center Ischemic Stroke Classification IEEE Access ·Mahesh Anil Inamdar,Anjan Gudigar,U. Raghavendra,Massimo Salvi,Raja Rizal Azman Bin Raja Aman,Nadia Fareeda Muhammad Gowdh,Izzah Amirah Binti Mohd Ahir,Mohd Salahuddin Bin Kamaruddin,Khairul Azmi Abdul Kadir,Filippo Molinari,Ajay Hegde,Girish Menon,U. Rajendra Acharya	2025	2
2	Directional-Guided Motion Sensitive Descriptor for Automated Detection of Hypertension Using Ultrasound Images IEEE Access ·Anjan Gudigar,Nahrizul Adib Kadri,U. Raghavendra,Jyothi Samanth,Mahesh Anil Inamdar,Mukund A. Prabhu,U. Rajendra Acharya	2024	3
3	Automated System for the Detection of Heart Anomalies Using Phonocardiograms: A Systematic Review IEEE Access ·Anjan Gudigar,U. Raghavendra,M. Maithri,Jyothi Samanth,Mahesh Anil Inamdar,V Vidhya,V. Jahmunah,Mukund A. Prabhu,Ru‐San Tan,Chai Hong Yeong,Filippo Molinari,U. Rajendra Acharya	2024	2
4	A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images IEEE Access ·Mahesh Anil Inamdar,U. Raghavendra,Anjan Gudigar,Sarvesh Bhandary,Massimo Salvi,Ravinesh C. Deo,Prabal Datta Barua,Edward J. Ciaccio,Filippo Molinari,U. Rajendra Acharya	2023	7
5	YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for Types of Intracranial Hematoma in CT Images IEEE Access ·V Vidhya,U. Raghavendra,Anjan Gudigar,Sudipta Basak,Sankalp Mallappa,Ajay Hegde,Girish Menon,Prabal Datta Barua,Massimo Salvi,Edward J. Ciaccio,Filippo Molinari,U. Rajendra Acharya	2023	5
6	FFCAEs: An efficient feature fusion framework using cascaded autoencoders for the identification of gliomas International Journal of Imaging Systems and Technology ·Anjan Gudigar,U. Raghavendra,Tejaswi N. Rao,Jyothi Samanth,V. Rajinikanth,Suresh Chandra Satapathy,Edward J. Ciaccio,Chan Wai Yee,U. Rajendra Acharya	2022	6
7	Novel Hypertrophic Cardiomyopathy Diagnosis Index Using Deep Features and Local Directional Pattern Techniques Journal of Imaging ·Anjan Gudigar,U. Raghavendra,Jyothi Samanth,Chinmay Dharmik,Mokshagna Rohit Gangavarapu,Krishnananda Nayak,Edward J. Ciaccio,Ru‐San Tan,Filippo Molinari,U. Rajendra Acharya	2022	9
8	Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques Informatics ·V Vidhya,U. Raghavendra,Anjan Gudigar,Praneet Kasula,Yashas Chakole,Ajay Hegde,Girish Menon,Chui Ping Ooi,Edward J. Ciaccio,U. Rajendra Acharya	2022	4
9	Novel Radiomics Features for Automated Detection of Cardiac Abnormality in Patients with Pacemaker Computational and Mathematical Methods in Medicine ·M. Umesh Pai,Ali Abbasian Ardakani,Aditya Kamath,U. Raghavendra,Anjan Gudigar,Naveen Venkatesh Sridharan,Jyothi Samanth,Tom Devasia,Mukund A. Prabhu,Niranjana Sampathila,G. Muralidhar Bairy	2022	2
10	Role of Four-Chamber Heart Ultrasound Images in Automatic Assessment of Fetal Heart: A Systematic Understanding Informatics ·Anjan Gudigar,U. Raghavendra,Jyothi Samanth,Akhila Vasudeva,A. A. J. Ashwal,Krishnananda Nayak,Ru‐San Tan,Edward J. Ciaccio,Chui Ping Ooi,Prabal Datta Barua,Filippo Molinari,U. Rajendra Acharya	2022	9
11	Motion pattern-based crowd scene classification using histogram of angular deviations of trajectories The Visual Computer ·Abhilash K. Pai,Prahaladh Chandrahasan,U. Raghavendra,A. K. Karunakar	2022	4
12	Role of Artificial Intelligence in COVID-19 Detection Sensors ·Anjan Gudigar,U. Raghavendra,Sneha Nayak,Chui Ping Ooi,Wai Yee Chan,Mokshagna Rohit Gangavarapu,Chinmay Dharmik,Jyothi Samanth,Nahrizul Adib Kadri,Khairunnisa Hasikin‬,Prabal Datta Barua,Subrata Chakraborty,Edward J. Ciaccio,U. Rajendra Acharya	2021	34
13	Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives International Journal of Environmental Research and Public Health ·V Vidhya,Anjan Gudigar,U. Raghavendra,Ajay Hegde,Girish Menon,Filippo Molinari,Edward J. Ciaccio,U. Rajendra Acharya	2021	44
14	A Review on Computer Aided Diagnosis of Acute Brain Stroke Sensors ·Mahesh Anil Inamdar,U. Raghavendra,Anjan Gudigar,Yashas Chakole,Ajay Hegde,Girish Menon,Prabal Datta Barua,Elizabeth E. Palmer,Kang Hao Cheong,Wai Yee Chan,Edward J. Ciaccio,U. Rajendra Acharya	2021	37
15	Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization International Journal of Environmental Research and Public Health ·Anjan Gudigar,Sneha Nayak,Jyothi Samanth,U. Raghavendra,A.J. Ashwal,Prabal Datta Barua,Md Nazmul Hasan,Edward J. Ciaccio,Ru‐San Tan,U. Rajendra Acharya	2021	22
16	Local Preserving Class Separation Framework to Identify Gestational Diabetes Mellitus Mother Using Ultrasound Fetal Cardiac Image IEEE Access ·Anjan Gudigar,Jyothi Samanth,U. Raghavendra,Chinmay Dharmik,Akhila Vasudeva,Padmakumar Ramachandran,Ru‐San Tan,Edward J. Ciaccio,Filippo Molinari,U. Rajendra Acharya	2020	11
17	Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus imagesbreakdown → Information Sciences ·U. Raghavendra,Hamido Fujita,Sulatha V. Bhandary,Anjan Gudigar,Jen Hong Tan,U. Rajendra Acharya	2018	336
18	Illumination Invariant Data Cost using Modified Census Transform U. Raghavendra,Krishnamoorthi Makkithaya,A. K. Karunakar	2014	1
19	Teaching Learning in Biochemistry: Medical College Students’ Perceptions and Opinions Education in Medicine Journal ·Jyothi MP D'Souza,U. Raghavendra,Deepak Herald D’Souza,Neevan D.R D'souza	2013	1
20	Saliva C- reactive protein levels in patients with acute urticaria Biomedical Research-tokyo ·Nivedita L. Rao,Sukanya Shetty,Jyothi D Souza,PRASAD RAO M,B Vishal,Shariff M. H,U. Raghavendra	2011	5

About U. Raghavendra

U. Raghavendra is a scholar working on Neurology, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition, having authored 84 papers that have together received 3.1k indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (15 papers), Retinal Imaging and Analysis (10 papers), AI in cancer detection (10 papers), Acute Ischemic Stroke Management (7 papers), Intracerebral and Subarachnoid Hemorrhage Research (6 papers), COVID-19 diagnosis using AI (6 papers), Advanced Neural Network Applications (6 papers) and Digital Imaging for Blood Diseases (5 papers). The work is most often cited by research in Health Informatics (92 citations), Radiology, Nuclear Medicine and Imaging (1.2k citations) and Neurology (422 citations). U. Raghavendra has collaborated with scholars based in India, Singapore and Malaysia. Frequent co-authors include U. Rajendra Acharya, Anjan Gudigar, Jen Hong Tan, Hamido Fujita, Yuki Hagiwara, Sulatha V. Bhandary, Shu Lih Oh, Edward J. Ciaccio, M. Murugappan and N. Arunkumar. Their work appears in journals such as IEEE Access, Sensors and Information Sciences.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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