Srikanth Prabhu

1.9k total citations
90 papers, 1.1k citations indexed

About

Srikanth Prabhu is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Srikanth Prabhu has authored 90 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Artificial Intelligence, 29 papers in Radiology, Nuclear Medicine and Imaging and 25 papers in Computer Vision and Pattern Recognition. Recurrent topics in Srikanth Prabhu's work include Artificial Intelligence in Healthcare (16 papers), COVID-19 diagnosis using AI (15 papers) and Digital Imaging for Blood Diseases (12 papers). Srikanth Prabhu is often cited by papers focused on Artificial Intelligence in Healthcare (16 papers), COVID-19 diagnosis using AI (15 papers) and Digital Imaging for Blood Diseases (12 papers). Srikanth Prabhu collaborates with scholars based in India, United States and Mexico. Srikanth Prabhu's co-authors include Krishnaraj Chadaga, Niranjana Sampathila, K. S. Swathi, Rajagopala Chadaga, Mamatha Balachandra, Shashikiran Umakanth, Vivekananda Bhat K, Krishna Prakash, Saptarshi Sengupta and Vinod C Nayak and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.

In The Last Decade

Srikanth Prabhu

77 papers receiving 1.1k citations

Peers

Srikanth Prabhu

RNMI

CVPR

HIM

Niranjana Sampathila India

Krishnaraj Chadaga India

Md Manjurul Ahsan United States

Md Khairul Islam Bangladesh

Muhammad Almas Anjum Pakistan

Parvathaneni Naga Srinivasu India

Hossam Magdy Balaha Egypt

Sagar Dhanraj Pande India

Muhammad Attique Khan Saudi Arabia

Kishor Datta Gupta United States

Niranjana Sampathila India

Srikanth Prabhu

356 ×1.1

311 ×1.0

RNMI

291 ×1.2

CVPR

48 ×0.4

102 ×1.0

HIM

Citations per year, relative to Srikanth Prabhu Srikanth Prabhu (= 1×) peers Niranjana Sampathila

Countries citing papers authored by Srikanth Prabhu

Since Specialization

Citations

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

Fields of papers citing papers by Srikanth Prabhu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Srikanth Prabhu

This figure shows the co-authorship network connecting the top 25 collaborators of Srikanth Prabhu. A scholar is included among the top collaborators of Srikanth Prabhu 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 Srikanth Prabhu. Srikanth Prabhu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Prakash, Krishna, et al.. (2025). Secure Knuckle Print Authentication: Template Protection and Attack Analysis. IEEE Access. 13. 144560–144577.

Singh, Nagendra Kumar, et al.. (2024). IoT-based greenhouse technologies for enhanced crop production: a comprehensive study of monitoring, control, and communication techniques. Systems Science & Control Engineering. 12(1). 13 indexed citations

Sharma, Akhilesh, et al.. (2024). A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images. Cogent Engineering. 11(1). 14 indexed citations

Chadaga, Krishnaraj, et al.. (2024). Demystifying multiple sclerosis diagnosis using interpretable and understandable artificial intelligence. Journal of Intelligent Systems. 33(1).

Prabhu, Srikanth, et al.. (2024). Classification of Surya Namaskar Yoga Asanas: A Sequential Combination of Predominant Poses for Physical and Mental Health. IEEE Access. 12. 102027–102034. 1 indexed citations

Chadaga, Krishnaraj, Srikanth Prabhu, Niranjana Sampathila, Rajagopala Chadaga, & Shashikiran Umakanth. (2024). An Explainable Decision Support Framework for Differential Diagnosis Between Mild COVID-19 and Other Similar Influenzas. IEEE Access. 12. 75010–75033. 3 indexed citations

Chadaga, Krishnaraj, et al.. (2024). Explainable artificial intelligence-driven gestational diabetes mellitus prediction using clinical and laboratory markers. Cogent Engineering. 11(1). 8 indexed citations

Prakash, Krishna, et al.. (2024). Deep Learning Applications in ECG Analysis and Disease Detection: An Investigation Study of Recent Advances. IEEE Access. 12. 126258–126284. 14 indexed citations

Chadaga, Krishnaraj, et al.. (2023). A decision support system for osteoporosis risk prediction using machine learning and explainable artificial intelligence. Heliyon. 9(12). e22456–e22456. 26 indexed citations

10.

Chadaga, Krishnaraj, Srikanth Prabhu, Niranjana Sampathila, & Rajagopala Chadaga. (2023). A machine learning and explainable artificial intelligence approach for predicting the efficacy of hematopoietic stem cell transplant in pediatric patients. SHILAP Revista de lepidopterología. 3. 100170–100170. 17 indexed citations

11.

Chadaga, Krishnaraj, et al.. (2023). A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome. Applied System Innovation. 6(2). 32–32. 55 indexed citations

12.

Chadaga, Krishnaraj, Srikanth Prabhu, Vivekananda Bhat K, et al.. (2023). Artificial intelligence for diagnosis of mild–moderate COVID-19 using haematological markers. Annals of Medicine. 55(1). 2233541–2233541. 19 indexed citations

13.

Chadaga, Krishnaraj, Srikanth Prabhu, Niranjana Sampathila, et al.. (2023). Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review. Diagnostics. 13(5). 824–824. 52 indexed citations

14.

Joshua, Abraham M., et al.. (2022). Effectiveness of balance training on pain and functional outcomes in knee osteoarthritis: A systematic review and meta-analysis. F1000Research. 11. 598–598. 3 indexed citations

15.

Chadaga, Krishnaraj, et al.. (2022). Classification of Malaria Using Object Detection Models. Informatics. 9(4). 76–76. 39 indexed citations

16.

Sampathila, Niranjana, Krishnaraj Chadaga, Rajagopala Chadaga, et al.. (2022). Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images. Healthcare. 10(10). 1812–1812. 63 indexed citations

17.

Chadaga, Krishnaraj, et al.. (2022). Diagnosing COVID-19 using artificial intelligence: a comprehensive review. Network Modeling Analysis in Health Informatics and Bioinformatics. 11(1). 30 indexed citations

18.

Chadaga, Krishnaraj, et al.. (2021). Battling COVID-19 using machine learning: A review. Cogent Engineering. 8(1). 24 indexed citations

19.

Prabhu, Srikanth, et al.. (2020). Improving the Performance of Convolutional Neural Network for the Segmentation of Optic Disc in Fundus Images Using Attention Gates and Conditional Random Fields. IEEE Access. 8. 29299–29310. 38 indexed citations

20.

Balachandra, Mamatha, et al.. (2019). Raspberry Pi as Visual Sensor Nodes in Precision Agriculture: A Study. IEEE Access. 7. 45110–45122. 63 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.

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