Tallha Akram
- Artificial Intelligence top 0.5%
- Computer Vision and Pattern Recognition top 1%
- Oncology top 2%
- Plant Science top 5%
- Radiology, Nuclear Medicine and Imaging top 2%
- Co-authors
- Muhammad Attique KhanMuhammad SharifTanzila SabaYudong ZhangKashif JavedAmjad RehmanSyed Rameez NaqviMussarat Yasmin
- Topics
- Cutaneous Melanoma Detection and Management (21 papers)AI in cancer detection (18 papers)Smart Agriculture and AI (11 papers)
- Partner nations
- PakistanSaudi ArabiaSouth Korea
In The Last Decade
Tallha Akram
84 papers receiving 3.6k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Artificial Intelligence 1.5k
- Computer Vision and Pattern Recognition 1.2k
- Oncology 1.1k
- Plant Science 591
- Radiology, Nuclear Medicine and Imaging 541
Countries citing papers authored by Tallha Akram
This map shows the geographic impact of Tallha Akram'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 Tallha Akram with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tallha Akram more than expected).
Fields of papers citing papers by Tallha Akram
This network shows the impact of papers produced by Tallha Akram. 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 Tallha Akram. The network helps show where Tallha Akram may publish in the future.
Co-authorship network of co-authors of Tallha Akram
This figure shows the co-authorship network connecting the top 25 collaborators of Tallha Akram. A scholar is included among the top collaborators of Tallha Akram 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 Tallha Akram. Tallha Akram is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 25 | |
| 3 | 10 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 32 | |
| 8 | 2 | |
| 9 | 12 | |
| 10 | 2 | |
| 11 | Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning frameworkbreakdown → | 198 |
| 12 | 91 | |
| 13 | 4 | |
| 14 | 73 | |
| 15 | 52 | |
| 16 | 9 | |
| 17 | 47 | |
| 18 | 83 | |
| 19 | 104 | |
| 20 | 9 |
About Tallha Akram
Tallha Akram is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Oncology, having authored 90 papers that have together received 3.9k indexed citations. Recurring topics across this work include Cutaneous Melanoma Detection and Management (21 papers), AI in cancer detection (18 papers) and Smart Agriculture and AI (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.2k citations), Artificial Intelligence (1.5k citations) and Oncology (1.1k citations). Tallha Akram has collaborated with scholars based in Pakistan, Saudi Arabia and South Korea. Frequent co-authors include Muhammad Attique Khan, Muhammad Sharif, Tanzila Saba, Muhammad Sharif, Yudong Zhang, Kashif Javed, Amjad Rehman, Syed Rameez Naqvi, Mussarat Yasmin and Muhammad Awais. 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.