U. Snekhalatha

1.7k total citations
84 papers, 1.1k citations indexed

About

U. Snekhalatha is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Rheumatology. According to data from OpenAlex, U. Snekhalatha has authored 84 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Radiology, Nuclear Medicine and Imaging, 19 papers in Biomedical Engineering and 14 papers in Rheumatology. Recurrent topics in U. Snekhalatha's work include Infrared Thermography in Medicine (28 papers), Advanced Chemical Sensor Technologies (13 papers) and Rheumatoid Arthritis Research and Therapies (12 papers). U. Snekhalatha is often cited by papers focused on Infrared Thermography in Medicine (28 papers), Advanced Chemical Sensor Technologies (13 papers) and Rheumatoid Arthritis Research and Therapies (12 papers). U. Snekhalatha collaborates with scholars based in India, Philippines and Saudi Arabia. U. Snekhalatha's co-authors include Ramji Kalidoss, Palani Thanaraj Krishnan, M. Anburajan, B. Venkatraman, M. Menaka, Yuvaraj Sivalingam, Parvathavarthini Balasubramanian, R. Anandan, Baldev Raj and K. Radhakrishnan and has published in prestigious journals such as PLoS ONE, Analytical Chemistry and Analytical Biochemistry.

In The Last Decade

U. Snekhalatha

78 papers receiving 1.0k citations

Peers

U. Snekhalatha

RNMI

EEE

RHEUM

BIOEN

Huiquan Wang China

Tong Liu China

Yoon‐Chul Kim South Korea

Zedong Nie China

Won Lee South Korea

Gang Cui China

Francisco J. Romero Spain

Junlin Zhang China

Xinke Li China

Yu Geng China

Huiquan Wang China

U. Snekhalatha

228 ×0.8

RNMI

384 ×1.5

137 ×0.7

EEE

19 ×0.1

RHEUM

13 ×0.1

BIOEN

Citations per year, relative to U. Snekhalatha U. Snekhalatha (= 1×) peers Huiquan Wang

Countries citing papers authored by U. Snekhalatha

Since Specialization

Citations

This map shows the geographic impact of U. Snekhalatha'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. Snekhalatha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites U. Snekhalatha more than expected).

Fields of papers citing papers by U. Snekhalatha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by U. Snekhalatha. 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. Snekhalatha. The network helps show where U. Snekhalatha may publish in the future.

Co-authorship network of co-authors of U. Snekhalatha

This figure shows the co-authorship network connecting the top 25 collaborators of U. Snekhalatha. A scholar is included among the top collaborators of U. Snekhalatha based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with U. Snekhalatha. U. Snekhalatha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Snekhalatha, U., et al.. (2025). Binary and Multi-Class Classification of Colorectal Polyps Using CRP-ViT: A Comparative Study Between CNNs and QNNs. Life. 15(7). 1124–1124.

Snekhalatha, U., et al.. (2025). Diabetic Retinopathy Detection, Segmentation and Classification Using YOLO v8, MultiResUNet and ResNet 18. 420–425.

Snekhalatha, U., et al.. (2024). ADVANCING COLORECTAL POLYP DETECTION: AN AUTOMATED SEGMENTATION APPROACH WITH COLRECTSEG-UNET. Biomedical Engineering Applications Basis and Communications. 36(6). 3 indexed citations

Snekhalatha, U., et al.. (2024). Early detection of Alzheimer’s disease in structural and functional MRI. Frontiers in Medicine. 11. 1520878–1520878. 2 indexed citations

Snekhalatha, U., et al.. (2024). Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques. Scientific Reports. 14(1). 14571–14571. 8 indexed citations

Snekhalatha, U., et al.. (2024). Recent advancements in non-invasive wearable electrochemical biosensors for biomarker analysis – A review. Analytical Biochemistry. 692. 115578–115578. 14 indexed citations

Snekhalatha, U., et al.. (2024). Artificial intelligence based real time colorectal cancer screening study: Polyp segmentation and classification using multi-house database. Biomedical Signal Processing and Control. 99. 106928–106928. 14 indexed citations

Snekhalatha, U., et al.. (2024). Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence. Biomedical Physics & Engineering Express. 11(1). 15015–15015. 1 indexed citations

Snekhalatha, U., et al.. (2024). Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques. Biomedical Physics & Engineering Express. 10(4). 45058–45058. 4 indexed citations

10.

Snekhalatha, U., et al.. (2023). Automated diagnosis of Retinopathy of prematurity from retinal images of preterm infants using hybrid deep learning techniques. Biomedical Signal Processing and Control. 85. 104883–104883. 13 indexed citations

11.

Snekhalatha, U., et al.. (2023). RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images. Scientific Reports. 13(1). 15638–15638. 14 indexed citations

12.

Snekhalatha, U., et al.. (2023). CRPU-NET: a deep learning model based semantic segmentation for the detection of colorectal polyp in lower gastrointestinal tract. Biomedical Physics & Engineering Express. 10(1). 15018–15018. 14 indexed citations

13.

Snekhalatha, U., et al.. (2023). Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques. Diagnostics. 13(18). 2987–2987. 10 indexed citations

14.

Snekhalatha, U., et al.. (2022). Multi-stage classification of Alzheimer’s disease from 18F-FDG-PET images using deep learning techniques. Physical and Engineering Sciences in Medicine. 45(4). 1301–1315. 6 indexed citations

15.

Snekhalatha, U., et al.. (2022). Automated evaluation of rheumatoid arthritis from hand radiographs using Machine Learning and deep learning techniques. Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine. 236(8). 1238–1249. 11 indexed citations

16.

Snekhalatha, U., et al.. (2020). A computer aided diagnostic method for the evaluation of type II diabetes mellitus in facial thermograms. Physical and Engineering Sciences in Medicine. 43(3). 871–888. 20 indexed citations

17.

Kalidoss, Ramji, et al.. (2020). Adsorption kinetics feature extraction from breathprint obtained by graphene based sensors for diabetes diagnosis. Journal of Breath Research. 15(1). 16005–16005. 20 indexed citations

18.

Snekhalatha, U., et al.. (2016). Ultrasound Color Doppler Image Segmentation and Feature Extraction in MCP and Wrist Region in Evaluation of Rheumatoid Arthritis. Journal of Medical Systems. 40(9). 197–197. 5 indexed citations

19.

Snekhalatha, U., M. Anburajan, B. Venkatraman, & M. Menaka. (2012). Evaluation of complete Freund’s adjuvant-induced arthritis in a Wistar rat model. Zeitschrift für Rheumatologie. 72(4). 375–382. 107 indexed citations

20.

Snekhalatha, U. & M. Anburajan. (2011). Evaluation of functional ability of rheumatoid arthritis based on HAQ score and BMD among South Indian patients. Rheumatology International. 32(7). 1997–2004. 1 indexed citations

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.

Explore authors with similar magnitude of impact