Sanjay Saxena

1.9k total citations
78 papers, 952 citations indexed

About

Sanjay Saxena is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging and Neurology. According to data from OpenAlex, Sanjay Saxena has authored 78 papers receiving a total of 952 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computer Vision and Pattern Recognition, 28 papers in Radiology, Nuclear Medicine and Imaging and 23 papers in Neurology. Recurrent topics in Sanjay Saxena's work include Brain Tumor Detection and Classification (23 papers), Radiomics and Machine Learning in Medical Imaging (17 papers) and Advanced Neural Network Applications (15 papers). Sanjay Saxena is often cited by papers focused on Brain Tumor Detection and Classification (23 papers), Radiomics and Machine Learning in Medical Imaging (17 papers) and Advanced Neural Network Applications (15 papers). Sanjay Saxena collaborates with scholars based in India, United States and Italy. Sanjay Saxena's co-authors include Gopal Krishna Nayak, Biswajit Jena, Jasjit S. Suri, Luca Saba, Luca Saba, Suchismita Das, Sudip Paul, Manudeep Kalra, Neeraj Sharma and Mannudeep K. Kalra and has published in prestigious journals such as SHILAP Revista de lepidopterología, Neurocomputing and Cancers.

In The Last Decade

Sanjay Saxena

65 papers receiving 925 citations

Peers

Sanjay Saxena

RNMI

CVPR

NEURO

Jai Prashanth Rao Singapore

Zhicheng Jiao United States

Darvin Yi United States

Justin Ker Singapore

Jasjit S. Suri United States

Fausto Milletarì United States

Zekun Jiang China

Muhammed Yıldırım Türkiye

Biswajit Jena India

Tryphon Lambrou United Kingdom

Jai Prashanth Rao Singapore

Sanjay Saxena

555 ×1.5

RNMI

356 ×1.1

CVPR

515 ×2.0

219 ×0.9

NEURO

190 ×1.7

Citations per year, relative to Sanjay Saxena Sanjay Saxena (= 1×) peers Jai Prashanth Rao

Countries citing papers authored by Sanjay Saxena

Since Specialization

Citations

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

Fields of papers citing papers by Sanjay Saxena

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sanjay Saxena

This figure shows the co-authorship network connecting the top 25 collaborators of Sanjay Saxena. A scholar is included among the top collaborators of Sanjay Saxena 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 Sanjay Saxena. Sanjay Saxena 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

Tsai, Lung‐Wen, et al.. (2025). RFiLM U-Net: Radiomic Feature-Integrated Linear Modulation Network for Precise Liver Tumor Segmentation. Journal of Medical and Biological Engineering. 45(2). 177–186. 1 indexed citations

Pareek, Vikas, et al.. (2025). Fv‐SFL: A Contrastive Learning‐Based Feature Sharing Method for Reducing the Effect of Label Skewed Data Heterogeneity in Federated Medical Imaging. Concurrency and Computation Practice and Experience. 37(4-5).

Jena, Biswajit, Sanjay Saxena, & Sudip Paul. (2025). Machine Learning for Neurodegenerative Disorders.

Saxena, Sanjay, et al.. (2025). AEGIS FLARE: IOT-ENABLED ROBOTIC FIREFIGHTER FOR ADVANCED FIRE DETECTION AND SUPPRESSION. Archives for Technical Sciences. 32(1). 66–74.

Panda, Soumyaranjan, et al.. (2024). Integrative data analysis of MGMT methylation and IDH1 mutation in glioblastoma: A comprehensive review. Elsevier eBooks. 181–202. 1 indexed citations

Saxena, Sanjay, et al.. (2024). Optimizing Glioblastoma, IDH-wildtype Treatment Outcomes : A Radiomics and Support Vector Machine-Based Approach to Overall Survival Estimation. Journal of Korean Neurosurgical Society. 68(1). 7–18. 1 indexed citations

Das, Suchismita, Rajni Dubey, Biswajit Jena, Lung‐Wen Tsai, & Sanjay Saxena. (2024). WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans. Multimedia Tools and Applications. 83(28). 71885–71908. 3 indexed citations

Saxena, Sanjay, Biswajit Jena, Neha Gupta, et al.. (2023). Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation. Computers in Biology and Medicine. 153. 106492–106492. 27 indexed citations

Paul, Sudip, Mahesh Maindarkar, Sanjay Saxena, et al.. (2022). Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review. Diagnostics. 12(1). 166–166. 33 indexed citations

10.

Suri, Jasjit S., Sudip Paul, Mahesh Maindarkar, et al.. (2022). Cardiovascular/Stroke Risk Stratification in Parkinson’s Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review. Metabolites. 12(4). 312–312. 26 indexed citations

11.

Suri, Jasjit S., Mrinalini Bhagawati, Sudip Paul, et al.. (2022). Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review. Computers in Biology and Medicine. 142. 105204–105204. 47 indexed citations

12.

Das, Suchismita, Gopal Krishna Nayak, Luca Saba, et al.. (2022). An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review. Computers in Biology and Medicine. 143. 105273–105273. 86 indexed citations

13.

Suri, Jasjit S., Mrinalini Bhagawati, Sudip Paul, et al.. (2022). A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review. Diagnostics. 12(3). 722–722. 27 indexed citations

14.

Saxena, Sanjay, Biswajit Jena, Neha Gupta, et al.. (2022). Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers. 14(12). 2860–2860. 66 indexed citations

15.

Sangeetha, K, et al.. (2021). Machine Learning-based Human Activity Recognition using Neighbourhood Component Analysis. 1080–1084. 4 indexed citations

16.

Dinesh-Kumar, S.P., et al.. (2021). Iot Based Smart Garbage Bin. NVEO - NATURAL VOLATILES & ESSENTIAL OILS Journal | NVEO. 3321–3332. 1 indexed citations

17.

Kazerooni, Anahita Fathi, Stephen Bagley, Hamed Akbari, et al.. (2021). Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine. Cancers. 13(23). 5921–5921. 42 indexed citations

18.

Jena, Biswajit, Gopal Krishna Nayak, & Sanjay Saxena. (2021). Convolutional neural network and its pretrained models for image classification and object detection: A survey. Concurrency and Computation Practice and Experience. 34(6). 37 indexed citations

19.

Prabha, D., et al.. (2020). A Study Of Detecting Malicious URL Using Convnet. International journal of scientific and technology research. 9(4). 752–755. 3 indexed citations

20.

Saxena, Sanjay, et al.. (2016). Parallel Image Processing Techniques, Benefits and Limitations. Research Journal of Applied Sciences Engineering and Technology. 12(2). 223–238. 15 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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