P. Mirunalini

404 total citations
15 papers, 239 citations indexed

About

P. Mirunalini is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, P. Mirunalini has authored 15 papers receiving a total of 239 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 4 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in P. Mirunalini's work include AI in cancer detection (3 papers), Medical Image Segmentation Techniques (2 papers) and Identification and Quantification in Food (2 papers). P. Mirunalini is often cited by papers focused on AI in cancer detection (3 papers), Medical Image Segmentation Techniques (2 papers) and Identification and Quantification in Food (2 papers). P. Mirunalini collaborates with scholars based in India and United States. P. Mirunalini's co-authors include S. M. Jaisakthi, D. Thenmozhi, Chandrabose Aravindan and Saima Sadiq and has published in prestigious journals such as Neural Computing and Applications, Multimedia Systems and IET Computer Vision.

In The Last Decade

P. Mirunalini

13 papers receiving 213 citations

Peers

P. Mirunalini

PS

ONCOL

AI

CVPR

AC

S. M. Jaisakthi India

Alfonso Medela Spain

Veysel Turk Türkiye

İsmail KUNDURACIOĞLU Türkiye

Muhammad Junaid Ali Pakistan

Jayant Jagtap India

Ulzii-Orshikh Dorj South Korea

Usharani Bhimavarapu India

Siddharth Gupta India

Suyash Bhardwaj India

S. M. Jaisakthi India

P. Mirunalini

153 ×1.6

PS

133 ×2.3

ONCOL

110 ×2.0

AI

54 ×1.3

CVPR

65 ×1.7

AC

Citations per year, relative to P. Mirunalini P. Mirunalini (= 1×) peers S. M. Jaisakthi

Countries citing papers authored by P. Mirunalini

Since Specialization

Citations

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

Fields of papers citing papers by P. Mirunalini

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of P. Mirunalini

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

All Works

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15 of 15 papers shown

1.

Mirunalini, P., et al.. (2024). Conditional adversarial segmentation and deep learning approach for skin lesion sub-typing from dermoscopic images. Neural Computing and Applications. 36(26). 16445–16463. 3 indexed citations

2.

Jaisakthi, S. M., et al.. (2023). A deep learning approach for nucleus segmentation and tumor classification from lung histopathological images. Network Modeling Analysis in Health Informatics and Bioinformatics. 12(1). 6 indexed citations

3.

Jaisakthi, S. M., et al.. (2021). Fish species recognition using transfer learning techniques. International Journal of Advances in Intelligent Informatics. 7(2). 188–188. 17 indexed citations

4.

Jaisakthi, S. M., P. Mirunalini, & Chandrabose Aravindan. (2019). Coral Reef Annotation and Localization using Faster R-CNN. CLEF (Working Notes). 2380. 1 indexed citations

5.

Jaisakthi, S. M., et al.. (2019). Grape Leaf Disease Identification using Machine Learning Techniques. 1–6. 95 indexed citations

6.

Mirunalini, P., et al.. (2019). Segmentation of Coronary Arteries from CTA axial slices using Deep Learning techniques. 2074–2080. 10 indexed citations

7.

Thenmozhi, D., et al.. (2019). MoneyBall - Data Mining on Cricket Dataset. 1–5. 15 indexed citations

8.

Mirunalini, P., et al.. (2018). Convolutional Long Short-Term Memory Neural Networks for Hierarchical Species Prediction.. CLEF (Working Notes). 2 indexed citations

9.

Jaisakthi, S. M., P. Mirunalini, & Chandrabose Aravindan. (2018). Automated skin lesion segmentation of dermoscopic images using GrabCut and k‐means algorithms. IET Computer Vision. 12(8). 1088–1095. 72 indexed citations

10.

Mirunalini, P., et al.. (2018). Audio Hiding in an Image Using Steganographic Methods. 11. 82–86. 1 indexed citations

11.

Mirunalini, P., et al.. (2017). Tracking of object in occluded and non-occluded environment using SIFT and Kalman filter. 1290–1295. 7 indexed citations

12.

Mirunalini, P., Chandrabose Aravindan, & S. M. Jaisakthi. (2017). Automatic stenosis detection using SVM from CTA projection images. Multimedia Systems. 25(2). 83–93. 5 indexed citations

13.

Thenmozhi, D., P. Mirunalini, & Chandrabose Aravindan. (2016). Decision Tree Approach for Consumer Health Information Search.. 221–225.

14.

Mirunalini, P. & S. M. Jaisakthi. (2015). 3D Coronary Artery Reconstruction using SVM. Research Journal of Applied Sciences Engineering and Technology. 11(7). 685–691. 1 indexed citations

15.

Mirunalini, P. & Chandrabose Aravindan. (2013). Automatic segmentation of coronary arteries and detection of stenosis. 1–4. 4 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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