Mohan Karnati

949 total citations · 1 hit paper
27 papers, 609 citations indexed

About

Mohan Karnati is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Mohan Karnati has authored 27 papers receiving a total of 609 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Mohan Karnati's work include Face and Expression Recognition (7 papers), AI in cancer detection (6 papers) and EEG and Brain-Computer Interfaces (6 papers). Mohan Karnati is often cited by papers focused on Face and Expression Recognition (7 papers), AI in cancer detection (6 papers) and EEG and Brain-Computer Interfaces (6 papers). Mohan Karnati collaborates with scholars based in India, Czechia and Malaysia. Mohan Karnati's co-authors include Ayan Seal, Ondřej Krejcar, Anis Yazidi, Geet Sahu, Debotosh Bhattacharjee, Malay Kishore Dutta, Abhishek Gupta, Ritesh Maurya, Joanna Jaworek-Korjakowska and Enrique Herrera‐Viedma and has published in prestigious journals such as Scientific Reports, Applied Soft Computing and Knowledge-Based Systems.

In The Last Decade

Mohan Karnati

25 papers receiving 588 citations

Hit Papers

Understanding Deep Learni... 2023 2026 2024 2023 25 50 75
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.

  1. Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey
    2023 92 citations Mohan Karnati, Ayan Seal et al. IEEE Transactions on Instrumentation and Measurement profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Mohan Karnati 324 308 125 107 53 27 609
Dimitrios Kollias 447 1.4× 476 1.5× 110 0.9× 252 2.4× 129 2.4× 40 1.0k
Mahmoud Shoman 112 0.3× 204 0.7× 232 1.9× 142 1.3× 45 0.8× 14 519
Zibo Meng 856 2.6× 697 2.3× 100 0.8× 131 1.2× 24 0.5× 35 1.1k
Cheng Lu 324 1.0× 359 1.2× 90 0.7× 263 2.5× 14 0.3× 62 764
Ronak Kosti 232 0.7× 188 0.6× 74 0.6× 101 0.9× 23 0.4× 9 371
Jingting Li 302 0.9× 212 0.7× 41 0.3× 132 1.2× 13 0.2× 35 455
Marc Oliu Simón 299 0.9× 270 0.9× 64 0.5× 45 0.4× 15 0.3× 3 426
Yuanyuan Shang 677 2.1× 411 1.3× 133 1.1× 156 1.5× 17 0.3× 37 1.1k
Adrià Recasens 330 1.0× 192 0.6× 94 0.8× 144 1.3× 9 0.2× 12 528

Countries citing papers authored by Mohan Karnati

Since Specialization
Citations

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

Fields of papers citing papers by Mohan Karnati

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohan Karnati

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

All Works

20 of 20 papers shown
1.
Karnati, Mohan, et al.. (2025). BMFCNet: Blended Multilevel Features With Constraint Fusion Network for Depression Detection From EEG Signals. IEEE Transactions on Instrumentation and Measurement. 74. 1–14. 1 indexed citations
2.
Karnati, Mohan, et al.. (2025). Fused multi-level attention features with a constraint fusion network for colorectal tissue classification using histopathological images. Computers & Electrical Engineering. 124. 110296–110296. 1 indexed citations
3.
Karnati, Mohan, et al.. (2025). Defect detection in 3D-printed polymer parts using deep learning models: a comparative investigation. Rapid Prototyping Journal. 31(7). 1428–1448. 1 indexed citations
4.
Karnati, Mohan, et al.. (2025). A Separable Bi-Pyramidal Feature Attention Network to Detect Alzheimer’s Using Electroencephalographic Signals. IEEE Transactions on Instrumentation and Measurement. 74. 1–15.
5.
Maurya, Ritesh, et al.. (2024). FCCS-Net: Breast cancer classification using Multi-Level fully Convolutional-Channel and spatial attention-based transfer learning approach. Biomedical Signal Processing and Control. 94. 106258–106258. 22 indexed citations
6.
Karnati, Mohan, et al.. (2024). Attention Based Deep Neural Network for Classification of Kidney Ailments Using CT Images. 1–5. 1 indexed citations
7.
Maurya, Ritesh, Satyajit Mahapatra, Malay Kishore Dutta, et al.. (2024). Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine. Scientific Reports. 14(1). 17785–17785. 5 indexed citations
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Karnati, Mohan, et al.. (2024). MD-DCNN: Multi-Scale Dilation-Based Deep Convolution Neural Network for epilepsy detection using electroencephalogram signals. Knowledge-Based Systems. 301. 112322–112322. 5 indexed citations
10.
Maurya, Ritesh, et al.. (2024). Breaking Barriers in Cancer Diagnosis: Super‐Light Compact Convolution Transformer for Colon and Lung Cancer Detection. International Journal of Imaging Systems and Technology. 34(5). 2 indexed citations
11.
Sahu, Geet, Mohan Karnati, Abhishek Gupta, & Ayan Seal. (2023). SCZ-SCAN: An automated Schizophrenia detection system from electroencephalogram signals. Biomedical Signal Processing and Control. 86. 105206–105206. 20 indexed citations
12.
Karnati, Mohan, Geet Sahu, Abhishek Gupta, Ayan Seal, & Ondřej Krejcar. (2023). A Pyramidal Spatial-Based Feature Attention Network for Schizophrenia Detection Using Electroencephalography Signals. IEEE Transactions on Cognitive and Developmental Systems. 16(3). 935–946. 16 indexed citations
13.
Karnati, Mohan, Ayan Seal, Debotosh Bhattacharjee, Anis Yazidi, & Ondřej Krejcar. (2023). Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey. IEEE Transactions on Instrumentation and Measurement. 72. 1–31. 92 indexed citations breakdown →
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Karnati, Mohan, et al.. (2023). Attention-Based VGG-Residual-Inception Module for EEG-Based Depression Detection. 33–37. 1 indexed citations
18.
Karnati, Mohan, Ayan Seal, Geet Sahu, Anis Yazidi, & Ondřej Krejcar. (2022). A novel multi-scale based deep convolutional neural network for detecting COVID-19 from X-rays. Applied Soft Computing. 125. 109109–109109. 32 indexed citations
19.
Seal, Ayan, Rishabh Bajpai, Mohan Karnati, et al.. (2022). Benchmarks for machine learning in depression discrimination using electroencephalography signals. Applied Intelligence. 53(10). 12666–12683. 28 indexed citations
20.
Karnati, Mohan, Ayan Seal, Anis Yazidi, & Ondřej Krejcar. (2021). LieNet: A Deep Convolution Neural Network Framework for Detecting Deception. IEEE Transactions on Cognitive and Developmental Systems. 14(3). 971–984. 57 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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