Vipin Venugopal

475 total citations · 1 hit paper
8 papers, 289 citations indexed

About

Vipin Venugopal is a scholar working on Oncology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Vipin Venugopal has authored 8 papers receiving a total of 289 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Oncology, 6 papers in Artificial Intelligence and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Vipin Venugopal's work include Cutaneous Melanoma Detection and Management (6 papers), AI in cancer detection (6 papers) and Nonmelanoma Skin Cancer Studies (2 papers). Vipin Venugopal is often cited by papers focused on Cutaneous Melanoma Detection and Management (6 papers), AI in cancer detection (6 papers) and Nonmelanoma Skin Cancer Studies (2 papers). Vipin Venugopal collaborates with scholars based in India. Vipin Venugopal's co-authors include Malaya Kumar Nath, Madhusudhan Mishra, Justin Joseph, Sathish Sankaran and P. Kuppusamy and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Computer Methods and Programs in Biomedicine.

In The Last Decade

Vipin Venugopal

6 papers receiving 274 citations

Hit Papers

align trajectories

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.

Hybrid convolutional neural networks with SVM classifier for classification of skin cancer

2022 149 citations Vipin Venugopal, Malaya Kumar Nath et al. SHILAP Revista de lepidopterología profile →

Peers

Vipin Venugopal

ONCOL

AI

EPIDE

CVPR

RNMI

Bill Cassidy United Kingdom

Xinzi He China

Thang Doan Canada

Zahra Mirikharaji Canada

Ivo M. Baltruschat Germany

Tehreem Syed Germany

Saket S. Chaturvedi India

Federico Pollastri Italy

Maryam Sadeghi Canada

Sergey Demyanov Australia

Bill Cassidy United Kingdom

Vipin Venugopal

110 ×0.6

ONCOL

112 ×0.7

AI

45 ×0.6

EPIDE

44 ×1.0

CVPR

34 ×0.9

RNMI

Citations per year, relative to Vipin Venugopal Vipin Venugopal (= 1×) peers Bill Cassidy

Countries citing papers authored by Vipin Venugopal

Since Specialization

Citations

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

Fields of papers citing papers by Vipin Venugopal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vipin Venugopal

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

All Works

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8 of 8 papers shown

1.

Kuppusamy, P., et al.. (2025). Probabilistic luminance estimation and optimized gamma correction for Wireless capsule endoscopy. Biomedical Signal Processing and Control. 104. 107558–107558.

2.

Venugopal, Vipin, et al.. (2025). EffNet-SVM: A Hybrid Model for Diabetic Retinopathy Classification Using Retinal Fundus Images. IEEE Access. 13. 79793–79804. 4 indexed citations

3.

Venugopal, Vipin, et al.. (2025). LeSegGAN: A Hybrid Attention-Based GAN for Accurate Lesion Segmentation in Dermatological Images. IEEE Access. 13. 177019–177035.

4.

Venugopal, Vipin, et al.. (2024). A deep learning-based illumination transform for devignetting photographs of dermatological lesions. Image and Vision Computing. 142. 104909–104909. 8 indexed citations

5.

Venugopal, Vipin, et al.. (2023). A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images. Decision Analytics Journal. 8. 100278–100278. 65 indexed citations

6.

Venugopal, Vipin, et al.. (2022). DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images. Computers in Biology and Medicine. 148. 105852–105852. 30 indexed citations

7.

Venugopal, Vipin, et al.. (2022). An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions. Computer Methods and Programs in Biomedicine. 222. 106935–106935. 33 indexed citations

8.

Venugopal, Vipin, et al.. (2022). Hybrid convolutional neural networks with SVM classifier for classification of skin cancer. SHILAP Revista de lepidopterología. 5. 100069–100069. 149 indexed citations breakdown →

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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