Dev Kumar Das

1.3k total citations
30 papers, 914 citations indexed

About

Dev Kumar Das is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Dev Kumar Das has authored 30 papers receiving a total of 914 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 8 papers in Media Technology. Recurrent topics in Dev Kumar Das's work include Digital Imaging for Blood Diseases (20 papers), AI in cancer detection (9 papers) and Image Processing Techniques and Applications (8 papers). Dev Kumar Das is often cited by papers focused on Digital Imaging for Blood Diseases (20 papers), AI in cancer detection (9 papers) and Image Processing Techniques and Applications (8 papers). Dev Kumar Das collaborates with scholars based in India. Dev Kumar Das's co-authors include Chandan Chakraborty, Madhumala Ghosh, Asok Kumar Maiti, Mallika Pal, Ajoy Kumar Ray, Pranab Kumar Dutta, Rajdeep Mukherjee, Bhaskar Mitra, Ananda Maiti and Naseeb Singh and has published in prestigious journals such as BioMed Research International, Applied Soft Computing and Journal of Microscopy.

In The Last Decade

Dev Kumar Das

29 papers receiving 870 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dev Kumar Das India 18 521 314 192 190 171 30 914
Muhammad Almas Anjum Pakistan 20 834 1.6× 564 1.8× 431 2.2× 43 0.2× 54 0.3× 55 1.5k
Joanna Jaworek-Korjakowska Poland 18 190 0.4× 334 1.1× 116 0.6× 66 0.3× 61 0.4× 45 812
Ali Mohammad Alqudah Jordan 21 242 0.5× 418 1.3× 415 2.2× 44 0.2× 34 0.2× 64 1.2k
‪Mohd Yusoff Mashor Malaysia 20 881 1.7× 523 1.7× 206 1.1× 302 1.6× 371 2.2× 151 1.4k
Flávio H. D. Araújo Brazil 18 499 1.0× 513 1.6× 444 2.3× 103 0.5× 57 0.3× 59 1.0k
Santiago Alférez Spain 16 678 1.3× 456 1.5× 234 1.2× 202 1.1× 97 0.6× 24 902
Çiğdem Gündüz-Demir Türkiye 17 543 1.0× 583 1.9× 286 1.5× 186 1.0× 105 0.6× 36 912
PM Taylor United Kingdom 10 558 1.1× 775 2.5× 347 1.8× 9 0.0× 74 0.4× 25 1.2k
Rajib Chakravorty Australia 13 61 0.1× 149 0.5× 90 0.5× 49 0.3× 21 0.1× 40 535
H.S. Bhadauria India 18 525 1.0× 351 1.1× 226 1.2× 95 0.5× 139 0.8× 62 920

Countries citing papers authored by Dev Kumar Das

Since Specialization
Citations

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

Fields of papers citing papers by Dev Kumar Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dev Kumar Das. 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 Dev Kumar Das. The network helps show where Dev Kumar Das may publish in the future.

Co-authorship network of co-authors of Dev Kumar Das

This figure shows the co-authorship network connecting the top 25 collaborators of Dev Kumar Das. A scholar is included among the top collaborators of Dev Kumar Das 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 Dev Kumar Das. Dev Kumar Das 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.
2.
Das, Dev Kumar & Pranab Kumar Dutta. (2018). Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches. Computers in Biology and Medicine. 104. 29–42. 28 indexed citations
3.
Das, Dev Kumar, et al.. (2017). Computational approach for mitotic cell detection and its application in oral squamous cell carcinoma. Multidimensional Systems and Signal Processing. 28(3). 1031–1050. 16 indexed citations
4.
Das, Dev Kumar, et al.. (2016). Fusion of Entropy-Based Thresholding and Active Contour Model for Detection of Exudate and Optic Disc in Color Fundus Images. Journal of Medical and Biological Engineering. 36(6). 795–809. 5 indexed citations
5.
Das, Dev Kumar, Rajdeep Mukherjee, & Chandan Chakraborty. (2015). Computational microscopic imaging for malaria parasite detection: a systematic review. Journal of Microscopy. 260(1). 1–19. 63 indexed citations
6.
Das, Dev Kumar, et al.. (2015). Automated identification of keratinization and keratin pearl area from in situ oral histological images. Tissue and Cell. 47(4). 349–358. 34 indexed citations
7.
8.
Das, Dev Kumar, Ananda Maiti, & Chandan Chakraborty. (2014). Automated system for characterization and classification of malaria‐infected stages using light microscopic images of thin blood smears. Journal of Microscopy. 257(3). 238–252. 40 indexed citations
9.
Mukherjee, Rashmi, Dhiraj Manohar Dhane, Dev Kumar Das, et al.. (2014). Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment. BioMed Research International. 2014. 1–9. 92 indexed citations
10.
Ghosh, Madhumala, Dev Kumar Das, Chandan Chakraborty, & Ajoy Kumar Ray. (2013). Quantitative characterisation of Plasmodium vivax in infected erythrocytes: a textural approach. 3(3). 203–203. 10 indexed citations
11.
Das, Dev Kumar, Chinmay Chakraborty, Bhaskar Mitra, Ananda Maiti, & A.K. Ray. (2012). Quantitative microscopy approach for shape‐based erythrocytes characterization in anaemia. Journal of Microscopy. 249(2). 136–149. 30 indexed citations
12.
Bhowmick, Sirsendu, Dev Kumar Das, Asok Kumar Maiti, & Chandan Chakraborty. (2012). Structural and textural classification of erythrocytes in anaemic cases: A scanning electron microscopic study. Micron. 44. 384–394. 20 indexed citations
13.
Bhowmick, Sirsendu, Dev Kumar Das, Asok Kumar Maiti, & Chandan Chakraborty. (2012). Computer-Aided Diagnosis of Thalassemia Using Scanning Electron Microscopic Images of Peripheral Blood: A Morphological Approach. Journal of Medical Imaging and Health Informatics. 2(3). 215–221. 8 indexed citations
14.
Das, Dev Kumar, Madhumala Ghosh, Mallika Pal, Asok Kumar Maiti, & Chandan Chakraborty. (2012). Machine learning approach for automated screening of malaria parasite using light microscopic images. Micron. 45. 97–106. 161 indexed citations
15.
Das, Dev Kumar, Asok Kumar Maiti, & Chandan Chakraborty. (2012). Textural Pattern Classification of Microscopic Images for Malaria Screening. 442–469. 3 indexed citations
16.
Das, Dev Kumar, et al.. (2011). Probabilistic prediction of malaria using morphological and textural information. 1–6. 23 indexed citations
17.
Ghosh, Madhumala, Dev Kumar Das, Ajoy Kumar Ray, & Chandan Chakraborty. (2011). Development of Renyi's Entropy Based Fuzzy Divergence Measure for Leukocyte Segmentation. Journal of Medical Imaging and Health Informatics. 1(4). 334–340. 6 indexed citations
18.
Ghosh, Madhumala, Dev Kumar Das, Chandan Chakraborty, & Ajoy Kumar Ray. (2010). Automated leukocyte recognition using fuzzy divergence. Micron. 41(7). 840–846. 78 indexed citations
19.
Ghosh, Madhumala, Dev Kumar Das, & Chandan Chakraborty. (2010). Entropy based divergence for leukocyte image segmentation. 409–413. 9 indexed citations
20.
Das, Dev Kumar, Naseeb Singh, & A. K. Sinha. (2006). A comparison of Fourier transform and wavelet transform methods for detection and classification of faults on transmission lines. 7 pp.–7 pp.. 54 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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