Basu Dev Shivahare

1.0k citations
33 papers · 432 · 2 hit papers · h-index 10

Impact in

Papers in

Basu Dev Shivahare

28 papers receiving 424 citations

Basu Dev Shivahare's Hit Papers

A hybrid deep CNN model for brain tumor image multi-classification 2024 · 68 citations
680+1Years since publication255075

Peers

Basu Dev Shivahare
Comparison fields: 5 of 77
  • Neurology 141
  • Computer Vision and Pattern Recognition 111
  • Health Information Management 22
  • Health Informatics 6
  • Artificial Intelligence 120
Replace Haytham Al-Feel with:
Haytham Al-Feel Egypt
Erdal Özbay Türkiye
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Asit Kumar Subudhi India
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Citations per year

Countries citing papers authored by Basu Dev Shivahare

Since Specialization
Citations

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

Fields of papers citing papers by Basu Dev Shivahare

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Basu Dev Shivahare. 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 Basu Dev Shivahare. The network helps show where Basu Dev Shivahare may publish in the future.

Co-authors

The 25 scholars most cited alongside Basu Dev Shivahare, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Basu Dev Shivahare Line = papers co-authored together Basu Dev Shivahare links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Employing deep learning and transfer learning for accurate brain tumor detection
Hit paper breakdown →
202482
2
A hybrid deep CNN model for brain tumor image multi-classification
Hit paper breakdown →
202468
3 202353
4 202441
5 202127
6 202419
7 202218
8 202117
9 202311
10 202410
11 20249
12 20158
13 20208
14 20228
15 20197
16 20237
17 20246
18 20245
19 20245
20 20225

About Basu Dev Shivahare

Basu Dev Shivahare is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology, Health Information Management and Information Systems, having authored 33 papers that have together received 432 indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (6 papers), COVID-19 diagnosis using AI (5 papers), Artificial Intelligence in Healthcare (5 papers), Metaheuristic Optimization Algorithms Research (5 papers), AI in cancer detection (4 papers), Digital Imaging for Blood Diseases (3 papers), Advanced Neural Network Applications (3 papers) and Image Enhancement Techniques (2 papers). The work is most often cited by research in Neurology (141 citations), Computer Vision and Pattern Recognition (111 citations), Health Information Management (22 citations), Health Informatics (6 citations) and Artificial Intelligence (120 citations). Basu Dev Shivahare has collaborated with scholars based in India, Saudi Arabia and Ethiopia. Frequent co-authors include Sandeep Kumar Mathivanan, Hariharan Rajadurai, Mohd Asif Shah, Saravanan Srinivasan, Prabhishek Singh, Manoj Diwakar, Hong Qin, Saurav Mallik, Vimal Bibhu and Radha Raman Chandan. Their work appears in journals such as Scientific Reports, PLoS ONE, Results in Engineering, Intelligence-Based Medicine and The Open Public Health Journal.

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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