Tallha Akram

5.1k total citations · 2 hit papers
90 papers, 3.9k citations indexed

About

Tallha Akram is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Oncology. According to data from OpenAlex, Tallha Akram has authored 90 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Computer Vision and Pattern Recognition, 26 papers in Artificial Intelligence and 24 papers in Oncology. Recurrent topics in Tallha Akram's work include Cutaneous Melanoma Detection and Management (21 papers), AI in cancer detection (18 papers) and Smart Agriculture and AI (11 papers). Tallha Akram is often cited by papers focused on Cutaneous Melanoma Detection and Management (21 papers), AI in cancer detection (18 papers) and Smart Agriculture and AI (11 papers). Tallha Akram collaborates with scholars based in Pakistan, Saudi Arabia and South Korea. Tallha Akram's 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 and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Access.

In The Last Decade

Tallha Akram

84 papers receiving 3.6k citations

Hit Papers

Attributes based skin lesion detection and recognition: A... 2021 2026 2022 2024 2021 2021 50 100 150

Peers

Tallha Akram
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
Replace Muhammad Sharif with:
Muhammad Sharif Pakistan
Khalid M. Hosny Egypt
Chen Li China
Mussarat Yasmin Pakistan
Sheifali Gupta India
Deepika Koundal India
Fayadh Alenezi Saudi Arabia
Mudassar Raza Pakistan
Zafer Cömert Türkiye
Zhiguo Jiang China
Muhammad Sharif Pakistan View profile →
Citations per field, relative to Tallha Akram
Tallha Akram · 1×
Citations per year, relative to Tallha Akram
Tallha Akram · 1×

Countries citing papers authored by Tallha Akram

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
# 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 framework breakdown →
198
12 91
13 4
14 73
15 52
16 9
17 47
18 83
19 104
20 9

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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2026