Ajay Mittal

1.8k total citations · 2 hit papers
48 papers, 1.0k citations indexed

About

Ajay Mittal is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Ajay Mittal has authored 48 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Computer Vision and Pattern Recognition, 16 papers in Artificial Intelligence and 16 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Ajay Mittal's work include COVID-19 diagnosis using AI (11 papers), AI in cancer detection (11 papers) and Digital Imaging for Blood Diseases (7 papers). Ajay Mittal is often cited by papers focused on COVID-19 diagnosis using AI (11 papers), AI in cancer detection (11 papers) and Digital Imaging for Blood Diseases (7 papers). Ajay Mittal collaborates with scholars based in India, United Kingdom and United States. Ajay Mittal's co-authors include Munish Kumar, Sanjeev Sofat, Rahul Hooda, Monika Sachdeva, Monika Bansal, Krishan Kumar, Veenu Rani, Manvjeet Kaur, Rajesh Bhatia and L.S. Davis and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, ACM Computing Surveys and Neural Computing and Applications.

In The Last Decade

Ajay Mittal

40 papers receiving 986 citations

Hit Papers

Transfer learning for image classification using VGG19: C... 2021 2026 2022 2024 2021 2023 50 100 150 200
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. Transfer learning for image classification using VGG19: Caltech-101 image data set
    2021 223 citations Monika Bansal, Munish Kumar et al. Journal of Ambient Intelligence and Humanized Computing profile →
  2. Self-supervised Learning: A Succinct Review
    2023 120 citations Veenu Rani, Munish Kumar et al. Archives of Computational Methods in Engineering profile →

Peers

Ajay Mittal
Zhitao Xiao China
Mohammad Farukh Hashmi India
Alex Noel Joseph Raj China
Alper Baştürk Türkiye
Qinghua Zhou China
Guangmin Sun China
Shuihua Wang United Kingdom
Xiang Yu China
Muhammad Arsalan South Korea
Bogdan J. Matuszewski United Kingdom
Zhitao Xiao China
Ajay Mittal
Citations per year, relative to Ajay Mittal Ajay Mittal (= 1×) peers Zhitao Xiao

Countries citing papers authored by Ajay Mittal

Since Specialization
Citations

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

Fields of papers citing papers by Ajay Mittal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ajay Mittal

This figure shows the co-authorship network connecting the top 25 collaborators of Ajay Mittal. A scholar is included among the top collaborators of Ajay Mittal 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 Ajay Mittal. Ajay Mittal 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.
Gupta, Aastha, et al.. (2025). A systematic review of end-to-end framework for contactless fingerprint recognition: Techniques, challenges, and future directions. Engineering Applications of Artificial Intelligence. 150. 110493–110493.
2.
Rani, Veenu, Munish Kumar, Aastha Gupta, et al.. (2024). Self-supervised learning for medical image analysis: a comprehensive review. Evolving Systems. 15(4). 1607–1633. 17 indexed citations
3.
4.
Mittal, Ajay, et al.. (2024). Deep residual learning-based denoiser for medical X-ray images. Evolving Systems. 15(6). 2339–2353.
5.
Singh, Gurpreet, Amit Chhabra, & Ajay Mittal. (2024). Evaluating Deep Learning Algorithms for MRI-Based Brain Tumor Classification. 428–434. 4 indexed citations
6.
7.
Alotaibi, Fahd S., et al.. (2024). TinyCheXReport: Compressed deep neural network for Chest X-ray report generation. ACM Transactions on Asian and Low-Resource Language Information Processing. 23(9). 1–17.
8.
Mittal, Ajay, et al.. (2023). LeukoCapsNet: a resource-efficient modified CapsNet model to identify leukemia from blood smear images. Neural Computing and Applications. 36(5). 2507–2524. 9 indexed citations
9.
Rani, Veenu, et al.. (2023). Self-supervised Learning: A Succinct Review. Archives of Computational Methods in Engineering. 30(4). 2761–2775. 120 indexed citations breakdown →
10.
Gupta, Savita, et al.. (2023). An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images. Wireless Personal Communications. 131(1). 197–215. 6 indexed citations
11.
Mittal, Ajay, et al.. (2022). CADxReport: Chest x-ray report generation using co-attention mechanism and reinforcement learning. Computers in Biology and Medicine. 145. 105498–105498. 21 indexed citations
12.
Mittal, Ajay, et al.. (2022). CheXPrune: sparse chest X-ray report generation model using multi-attention and one-shot global pruning. Journal of Ambient Intelligence and Humanized Computing. 14(6). 7485–7497. 11 indexed citations
13.
Mittal, Ajay, et al.. (2022). RadioBERT: A deep learning-based system for medical report generation from chest X-ray images using contextual embeddings. Journal of Biomedical Informatics. 135. 104220–104220. 21 indexed citations
14.
Mittal, Ajay, et al.. (2022). Automated Analysis of Blood Smear Images for Leukemia Detection: A Comprehensive Review. ACM Computing Surveys. 54(11s). 1–37. 18 indexed citations
15.
Bansal, Monika, Munish Kumar, Monika Sachdeva, & Ajay Mittal. (2021). Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of Ambient Intelligence and Humanized Computing. 14(4). 3609–3620. 223 indexed citations breakdown →
16.
Mittal, Ajay, et al.. (2020). ResDNN: deep residual learning for natural image denoising. IET Image Processing. 14(11). 2425–2434. 18 indexed citations
17.
Goyal, Rohit, et al.. (2019). Non Familial Cherubism: A Case Report. Indian Journal of Otolaryngology and Head & Neck Surgery. 71(S1). 865–867. 1 indexed citations
18.
Hooda, Rahul, Ajay Mittal, & Sanjeev Sofat. (2018). Segmentation of lung fields from chest radiographs-a radiomic feature-based approach. Biomedical Engineering Letters. 9(1). 109–117. 14 indexed citations
19.
Mittal, Ajay, et al.. (2018). 3D convolutional neural network for object recognition: a review. Multimedia Tools and Applications. 78(12). 15951–15995. 45 indexed citations
20.
Gupta, Abhinav, Ajay Mittal, & L.S. Davis. (2008). Constraint Integration for Efficient Multiview Pose Estimation with Self-Occlusions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 30(3). 493–506. 36 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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