Nitin Kumar Chauhan

419 total citations
9 papers, 273 citations indexed

About

Nitin Kumar Chauhan is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nitin Kumar Chauhan has authored 9 papers receiving a total of 273 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 4 papers in Radiology, Nuclear Medicine and Imaging and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nitin Kumar Chauhan's work include AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Brain Tumor Detection and Classification (2 papers). Nitin Kumar Chauhan is often cited by papers focused on AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Brain Tumor Detection and Classification (2 papers). Nitin Kumar Chauhan collaborates with scholars based in India, Lebanon and United Arab Emirates. Nitin Kumar Chauhan's co-authors include Krishna P. Singh, Mamta Mittal, D. Jude Hemanth, Seifedine Kadry, Shubham Mahajan, Dharmendra Kumar, Sachin Kumar Gupta, Krishna Kant Singh and Ashutosh Mishra and has published in prestigious journals such as Scientific Reports, BioMed Research International and Wireless Personal Communications.

In The Last Decade

Nitin Kumar Chauhan

7 papers receiving 265 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Nitin Kumar Chauhan India	5	109	43	37	27	24	9	273
Sania Anam Pakistan	6	77 0.7×	81 1.9×	37 1.0×	27 1.0×	19 0.8×	19	345
Ştefan Holban Romania	8	134 1.2×	59 1.4×	97 2.6×	57 2.1×	10 0.4×	53	350
Amani K. Samha Saudi Arabia	6	116 1.1×	40 0.9×	40 1.1×	23 0.9×	10 0.4×	14	323
Chandradeep Bhatt India	5	90 0.8×	68 1.6×	50 1.4×	23 0.9×	23 1.0×	63	260
Syed Inthiyaz India	9	71 0.7×	27 0.6×	63 1.7×	24 0.9×	13 0.5×	41	303
Damien Dablain United States	4	184 1.7×	25 0.6×	45 1.2×	9 0.3×	29 1.2×	4	337
Sashikanta Prusty India	6	80 0.7×	37 0.9×	18 0.5×	12 0.4×	20 0.8×	22	213
N Moradzadeh United States	3	165 1.5×	121 2.8×	21 0.6×	31 1.1×	19 0.8×	6	341

Countries citing papers authored by Nitin Kumar Chauhan

Since Specialization

Citations

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

Fields of papers citing papers by Nitin Kumar Chauhan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nitin Kumar Chauhan

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

All Works

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9 of 9 papers shown

Chauhan, Nitin Kumar, Krishna Kant Singh, Ashutosh Mishra, et al.. (2025). A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides. Scientific Reports. 15(1). 12801–12801. 3 indexed citations

Chauhan, Nitin Kumar, et al.. (2025). Evaluating Deep Learning Models for Brain Tumor Detection Utilizing Transfer Learning and Fine-Tuning Approach. 201–206.

Mittal, Mamta, et al.. (2024). Binary and Ternary Classifiers to Detect COVID-19 Patients Using Chest X-ray Images: An Efficient Layered CNN Approach. New Generation Computing.

Chauhan, Nitin Kumar, et al.. (2023). HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides. BioMed Research International. 2023(1). 4214817–4214817. 21 indexed citations

Chauhan, Nitin Kumar, et al.. (2023). An Evaluative Investigation of Deep Learning Models by Utilizing Transfer Learning and Fine-Tuning for Cervical Cancer Screening of Whole Slide Pap-Smear Images. 1–5. 4 indexed citations

Chauhan, Nitin Kumar & Krishna P. Singh. (2022). Performance Assessment of Machine Learning Classifiers Using Selective Feature Approaches for Cervical Cancer Detection. Wireless Personal Communications. 124(3). 2335–2366. 28 indexed citations

Chauhan, Nitin Kumar & Krishna P. Singh. (2022). Diagnosis of Cervical Cancer with Oversampled Unscaled and Scaled Data Using Machine Learning Classifiers. 2022 IEEE Delhi Section Conference (DELCON). 1–6. 5 indexed citations

Chauhan, Nitin Kumar & Krishna P. Singh. (2021). Impact of Variation in Number of Channels in CNN Classification model for Cervical Cancer Detection. 1–6. 10 indexed citations

Chauhan, Nitin Kumar & Krishna P. Singh. (2018). A Review on Conventional Machine Learning vs Deep Learning. 347–352. 202 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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