Pronab Ghosh
- Health Information Management top 0.2%
- Artificial Intelligence in Healthcare 14
- Health Informatics top 5%
- Neurology top 10%
- Artificial Intelligence top 5%
- Machine Learning in Healthcare 6
- AI in cancer detection 6
- Imbalanced Data Classification Techniques 4
-
- COVID-19 diagnosis using AI 3
- Radiomics and Machine Learning in Medical Imaging 2
-
- Liver Disease Diagnosis and Treatment 2
- Nonmelanoma Skin Cancer Studies 2
- Co-authors
- Sami AzamAsif KarimF. M. Javed Mehedi ShamratMirjam JonkmanFriso De BoerShahana ShultanaAbhijith Reddy BeeravoluEva Ignatious
- Partner nations
- BangladeshAustraliaCanada
In The Last Decade
Pronab Ghosh
23 papers receiving 921 citations
Hit Papers
Peers
Comparison fields: 5 of 119
- Health Information Management 395
- Health Informatics 35
- Neurology 132
- Artificial Intelligence 437
- Medical Laboratory Technology 20
Countries citing papers authored by Pronab Ghosh
This map shows the geographic impact of Pronab Ghosh'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 Pronab Ghosh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pronab Ghosh more than expected).
Fields of papers citing papers by Pronab Ghosh
This network shows the impact of papers produced by Pronab Ghosh. 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 Pronab Ghosh. The network helps show where Pronab Ghosh may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Pronab Ghosh, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 6 | |
| 2 | 2024 | 7 | |
| 3 | 2023 | 7 | |
| 4 | 2023 | 10 | |
| 5 | AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Imagesbreakdown → | 2023 | 101 |
| 6 | 2022 | 25 | |
| 7 | 2022 | 37 | |
| 8 | 2022 | 3 | |
| 9 | 2022 | 1 | |
| 10 | 2022 | 74 | |
| 11 | 2021 | 27 | |
| 12 | 2021 | 17 | |
| 13 | Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniquesbreakdown → | 2021 | 322 |
| 14 | 2021 | 76 | |
| 15 | 2021 | 9 | |
| 16 | 2021 | 35 | |
| 17 | 2021 | 30 | |
| 18 | 2020 | 37 | |
| 19 | 2020 | 55 | |
| 20 | 2019 | 6 |
About Pronab Ghosh
Pronab Ghosh is a scholar working on Health Information Management, Health Informatics and Medical Laboratory Technology, having authored 25 papers that have together received 967 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (14 papers), Machine Learning in Healthcare (6 papers), AI in cancer detection (6 papers), Imbalanced Data Classification Techniques (4 papers), COVID-19 diagnosis using AI (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Liver Disease Diagnosis and Treatment (2 papers) and Nonmelanoma Skin Cancer Studies (2 papers). The work is most often cited by research in Health Information Management (395 citations), Health Informatics (35 citations) and Neurology (132 citations). Pronab Ghosh has collaborated with scholars based in Bangladesh, Australia and Canada. Frequent co-authors include Sami Azam, Asif Karim, F. M. Javed Mehedi Shamrat, Mirjam Jonkman, Friso De Boer, Shahana Shultana, Abhijith Reddy Beeravolu, Eva Ignatious, Zarrin Tasnim and Khan Md. Hasib. Their work appears in journals such as PLoS ONE, Scientific Reports and IEEE Access.
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.