Debashis Nandi

1.3k total citations
66 papers, 866 citations indexed

About

Debashis Nandi is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Debashis Nandi has authored 66 papers receiving a total of 866 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Computer Vision and Pattern Recognition, 14 papers in Radiology, Nuclear Medicine and Imaging and 13 papers in Artificial Intelligence. Recurrent topics in Debashis Nandi's work include Radiomics and Machine Learning in Medical Imaging (11 papers), Image Enhancement Techniques (10 papers) and Medical Image Segmentation Techniques (9 papers). Debashis Nandi is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), Image Enhancement Techniques (10 papers) and Medical Image Segmentation Techniques (9 papers). Debashis Nandi collaborates with scholars based in India, United Arab Emirates and United States. Debashis Nandi's co-authors include Provas Kumar Roy, Susanta Dutta, Palash Ghosal, Mrinal Kanti Mandal, Jayasree Chakraborty, Gourab Dutta Banik, Anup Sadhu, Dipayan Das, Neha Upadhyay and Baisakhi Chakraborty and has published in prestigious journals such as Applied Soft Computing, International Journal of Electrical Power & Energy Systems and Computer Methods and Programs in Biomedicine.

In The Last Decade

Debashis Nandi

64 papers receiving 822 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Debashis Nandi India 17 420 208 190 171 149 66 866
Jingye Cai China 18 179 0.4× 183 0.9× 61 0.3× 229 1.3× 186 1.2× 76 776
Fatma Taher United Arab Emirates 21 297 0.7× 464 2.2× 130 0.7× 101 0.6× 483 3.2× 110 1.3k
Moumen El-Melegy Egypt 14 329 0.8× 187 0.9× 103 0.5× 41 0.2× 159 1.1× 72 663
Khan Bahadar Khan Pakistan 14 288 0.7× 113 0.5× 74 0.4× 106 0.6× 342 2.3× 33 766
M. Sultan Zia Pakistan 13 170 0.4× 140 0.7× 110 0.6× 214 1.3× 124 0.8× 25 704
M. Madheswaran India 17 274 0.7× 283 1.4× 38 0.2× 291 1.7× 159 1.1× 127 907
Maha Sharkas Egypt 17 318 0.8× 636 3.1× 168 0.9× 130 0.8× 523 3.5× 40 1.1k
Guitao Cao China 16 345 0.8× 252 1.2× 30 0.2× 45 0.3× 166 1.1× 84 924
Ahmed S. Salama Egypt 13 288 0.7× 269 1.3× 251 1.3× 40 0.2× 118 0.8× 76 794
H.S. Bhadauria India 18 525 1.3× 351 1.7× 88 0.5× 85 0.5× 226 1.5× 62 920

Countries citing papers authored by Debashis Nandi

Since Specialization
Citations

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

Fields of papers citing papers by Debashis Nandi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Debashis Nandi

This figure shows the co-authorship network connecting the top 25 collaborators of Debashis Nandi. A scholar is included among the top collaborators of Debashis Nandi 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 Debashis Nandi. Debashis Nandi 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.
Banerjee, S. K., et al.. (2025). Rim learning framework based on TS-GAN: A new paradigm of automated glaucoma screening from fundus images. Computers in Biology and Medicine. 187. 109752–109752. 4 indexed citations
2.
Nandi, Debashis, et al.. (2024). Deep Quasi-Recurrent Self-Attention With Dual Encoder-Decoder in Biomedical CT Image Segmentation. IEEE Journal of Biomedical and Health Informatics. 28(12). 7195–7205. 14 indexed citations
3.
Agarwal, Rohit, et al.. (2024). Multi-scale dual-channel feature embedding decoder for biomedical image segmentation. Computer Methods and Programs in Biomedicine. 257. 108464–108464. 28 indexed citations
4.
Mandal, Dipankar, et al.. (2024). Identifying the degree of cornstarch adulteration in turmeric powder using optimized convolutional neural network. Intelligent Decision Technologies. 18(3). 1955–1964. 1 indexed citations
5.
Saha, Sanjib, et al.. (2023). ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images. Biomedical Signal Processing and Control. 85. 104974–104974. 15 indexed citations
6.
7.
Nandi, Debashis, et al.. (2023). CCOCSA-based multi-frame sparse coding super-resolution via mutual information-based weighted image fusion. Multimedia Tools and Applications. 83(1). 2427–2471. 1 indexed citations
8.
Sarkar, Manas, et al.. (2023). Tetrolet Transform and Dual Dictionary Learning-Based Single Image Fog Removal. Arabian Journal for Science and Engineering. 48(8). 10771–10786. 2 indexed citations
9.
Ghosal, Palash, et al.. (2022). A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images. Computer Methods and Programs in Biomedicine. 226. 107157–107157. 18 indexed citations
10.
Dhara, Ashis Kumar, et al.. (2021). Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images. Pattern Recognition and Image Analysis. 31(4). 730–738. 6 indexed citations
11.
Dhara, Ashis Kumar, et al.. (2020). Deep learning for screening of interstitial lung disease patterns in high-resolution CT images. Clinical Radiology. 75(6). 481.e1–481.e8. 35 indexed citations
12.
Upadhyay, Neha, et al.. (2020). CSNet: A new DeepNet framework for ischemic stroke lesion segmentation. Computer Methods and Programs in Biomedicine. 193. 105524–105524. 66 indexed citations
13.
Ghosal, Palash, et al.. (2020). MhURI:A Supervised Segmentation Approach to Leverage Salient Brain Tissues in Magnetic Resonance Images. Computer Methods and Programs in Biomedicine. 200. 105841–105841. 12 indexed citations
14.
Ghosal, Palash, et al.. (2020). Breast Lesion Segmentation in Ultrasound Images Using Deep Convolutional Neural Networks. 318–322. 6 indexed citations
15.
Ghosal, Palash, et al.. (2019). A Dense U-Net Architecture for Multiple Sclerosis Lesion Segmentation. 662–667. 14 indexed citations
16.
Nandi, Debashis, et al.. (2017). Automated segmentation of lung field in HRCT images using active shape model. 2516–2520. 6 indexed citations
17.
Dutta, Susanta, et al.. (2016). Unified power flow controller based reactive power dispatch using oppositional krill herd algorithm. International Journal of Electrical Power & Energy Systems. 80. 10–25. 38 indexed citations
18.
Dutta, Susanta, Provas Kumar Roy, & Debashis Nandi. (2015). Optimal location of STATCOM using chemical reaction optimization for reactive power dispatch problem. Ain Shams Engineering Journal. 7(1). 233–247. 43 indexed citations
19.
20.
Mandal, Mrinal Kanti, et al.. (2011). Symmetric key chaotic image encryption using circle map. Indian Journal of Science and Technology. 4(5). 593–599. 20 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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