Farhat Afza

762 total citations
8 papers, 575 citations indexed

About

Farhat Afza is a scholar working on Computer Vision and Pattern Recognition, Oncology and Artificial Intelligence. According to data from OpenAlex, Farhat Afza has authored 8 papers receiving a total of 575 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Computer Vision and Pattern Recognition, 4 papers in Oncology and 3 papers in Artificial Intelligence. Recurrent topics in Farhat Afza's work include Cutaneous Melanoma Detection and Management (4 papers), Optical Coherence Tomography Applications (2 papers) and AI in cancer detection (2 papers). Farhat Afza is often cited by papers focused on Cutaneous Melanoma Detection and Management (4 papers), Optical Coherence Tomography Applications (2 papers) and AI in cancer detection (2 papers). Farhat Afza collaborates with scholars based in Pakistan, Saudi Arabia and United States. Farhat Afza's co-authors include Muhammad Attique Khan, Muhammad Sharif, Muhammad Sharif, Muhammad Rashid, U. John Tanik, Mussarat Yasmin, Amjad Rehman, Usman Tariq, Jaehyuk Cha and Hwan-Seung Yong and has published in prestigious journals such as Sensors, Methods and Image and Vision Computing.

In The Last Decade

Farhat Afza

8 papers receiving 543 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Farhat Afza Pakistan 7 285 234 222 99 92 8 575
Muhammad Rashid Pakistan 11 282 1.0× 300 1.3× 263 1.2× 80 0.8× 126 1.4× 14 682
Hiam Alquran Jordan 15 358 1.3× 122 0.5× 105 0.5× 131 1.3× 257 2.8× 71 816
Shafqat Ali Shad United States 8 274 1.0× 90 0.4× 343 1.5× 35 0.4× 155 1.7× 17 651
Ibtissam Bakkouri Morocco 8 150 0.5× 59 0.3× 115 0.5× 19 0.2× 94 1.0× 11 348
Hamidullah Binol United States 12 166 0.6× 45 0.2× 123 0.6× 11 0.1× 77 0.8× 33 467
S.A. Karkanis Greece 14 263 0.9× 451 1.9× 499 2.2× 18 0.2× 188 2.0× 40 987
Mohamed Maher Ben Ismail Saudi Arabia 13 127 0.4× 33 0.1× 145 0.7× 29 0.3× 31 0.3× 68 465
Walaa Gouda Saudi Arabia 10 267 0.9× 209 0.9× 104 0.5× 91 0.9× 163 1.8× 16 501
Surbhi Gupta India 15 268 0.9× 42 0.2× 83 0.4× 30 0.3× 188 2.0× 24 563
Farhan Akram Spain 13 244 0.9× 92 0.4× 224 1.0× 55 0.6× 147 1.6× 31 498

Countries citing papers authored by Farhat Afza

Since Specialization
Citations

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

Fields of papers citing papers by Farhat Afza

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Farhat Afza

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

All Works

8 of 8 papers shown
1.
Afza, Farhat, Muhammad Sharif, Muhammad Attique Khan, et al.. (2022). Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine. Sensors. 22(3). 799–799. 100 indexed citations
2.
Afza, Farhat, Muhammad Sharif, Mamta Mittal, Muhammad Attique Khan, & D. Jude Hemanth. (2021). A hierarchical three-step superpixels and deep learning framework for skin lesion classification. Methods. 202. 88–102. 85 indexed citations
3.
Afza, Farhat, et al.. (2021). Genome wide identification and analysis of WD40 domain containing proteins in Danio rerio. Gene Reports. 26. 101426–101426. 2 indexed citations
4.
Afza, Farhat, Muhammad Attique Khan, Muhammad Sharif, et al.. (2020). Skin Lesion Classification: An Optimized Framework of Optimal Color Features Selection. 1–6. 9 indexed citations
5.
Afza, Farhat, Muhammad Attique Khan, Muhammad Sharif, et al.. (2020). A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection. Image and Vision Computing. 106. 104090–104090. 102 indexed citations
6.
Afza, Farhat, Muhammad Attique Khan, Muhammad Sharif, & Amjad Rehman. (2019). Microscopic skin laceration segmentation and classification: A framework of statistical normal distribution and optimal feature selection. Microscopy Research and Technique. 82(9). 1471–1488. 66 indexed citations
7.
Sharif, Muhammad, Muhammad Attique Khan, Muhammad Rashid, et al.. (2019). Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images. Journal of Experimental & Theoretical Artificial Intelligence. 33(4). 577–599. 121 indexed citations
8.
Rashid, Muhammad, et al.. (2018). Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features. Multimedia Tools and Applications. 78(12). 15751–15777. 90 indexed citations

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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