Saqib Qamar

620 total citations
22 papers, 382 citations indexed

About

Saqib Qamar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Neurology. According to data from OpenAlex, Saqib Qamar has authored 22 papers receiving a total of 382 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 5 papers in Neurology. Recurrent topics in Saqib Qamar's work include Advanced Neural Network Applications (9 papers), Medical Image Segmentation Techniques (7 papers) and Brain Tumor Detection and Classification (5 papers). Saqib Qamar is often cited by papers focused on Advanced Neural Network Applications (9 papers), Medical Image Segmentation Techniques (7 papers) and Brain Tumor Detection and Classification (5 papers). Saqib Qamar collaborates with scholars based in China, Sweden and Saudi Arabia. Saqib Qamar's co-authors include Parvez Ahmad, Ran Zheng, Hai Jin, Linlin Shen, Mohd Usama, Salabat Khan, Salman Qadri, Muhammad Azeem Akbar, Maqbool Khan and Md Belal Bin Heyat and has published in prestigious journals such as Scientific Reports, IEEE Access and Future Generation Computer Systems.

In The Last Decade

Saqib Qamar

21 papers receiving 364 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Saqib Qamar China	10	160	119	86	79	60	22	382
Parvez Ahmad China	9	132 0.8×	95 0.8×	58 0.7×	69 0.9×	32 0.5×	17	297
Yunendah Nur Fuadah Indonesia	10	74 0.5×	91 0.8×	66 0.8×	47 0.6×	65 1.1×	36	359
Liangliang Liu China	8	175 1.1×	160 1.3×	117 1.4×	70 0.9×	49 0.8×	21	481
Yin Dai China	9	214 1.3×	162 1.4×	156 1.8×	78 1.0×	62 1.0×	14	455
Lisa Di Jorio Canada	5	133 0.8×	102 0.9×	163 1.9×	43 0.5×	75 1.3×	9	319
Talha Meraj Pakistan	13	122 0.8×	219 1.8×	240 2.8×	34 0.4×	42 0.7×	24	576
Abdulkader Helwan Cyprus	10	56 0.3×	132 1.1×	94 1.1×	44 0.6×	39 0.7×	22	330
Mohd Usama China	10	97 0.6×	249 2.1×	61 0.7×	38 0.5×	26 0.4×	16	492
Ferhat Bozkurt Türkiye	11	119 0.7×	122 1.0×	75 0.9×	34 0.4×	34 0.6×	38	288
Sarfaraz Hussein United States	7	79 0.5×	206 1.7×	248 2.9×	44 0.6×	43 0.7×	12	458

Countries citing papers authored by Saqib Qamar

Since Specialization

Citations

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

Fields of papers citing papers by Saqib Qamar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saqib Qamar

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Qamar, Saqib, et al.. (2025). Attention-driven UNet enhancement for accurate segmentation of bacterial spore outgrowth in microscopy images. Scientific Reports. 15(1). 20177–20177. 1 indexed citations

Qamar, Saqib, et al.. (2025). ScaleFusionNet: transformer-guided multi-scale feature fusion for skin lesion segmentation. Scientific Reports. 15(1). 34393–34393. 1 indexed citations

Qamar, Saqib, et al.. (2025). UNet with self-adaptive Mamba-like attention and causal-resonance learning for medical image segmentation. Scientific Reports. 16(1). 135–135. 1 indexed citations

Qamar, Saqib, et al.. (2024). Segmentation and characterization of macerated fibers and vessels using deep learning. Plant Methods. 20(1). 126–126. 5 indexed citations

Qamar, Saqib, et al.. (2023). A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images. Scientific Reports. 13(1). 18758–18758. 13 indexed citations

Qadri, Syed Furqan, Linlin Shen, Mubashir Ahmad, et al.. (2023). CT‐Based Automatic Spine Segmentation Using Patch‐Based Deep Learning. International Journal of Intelligent Systems. 2023(1). 66 indexed citations

Othman, Bestoon, et al.. (2022). An Empirical Analysis of Artificial Intelligence (AI) as a Growth Engine for the Healthcare Sector. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). 2454–2457. 15 indexed citations

Ahmad, Parvez, Hai Jin, Roobaea Alroobaea, et al.. (2021). MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation. IEEE Access. 9. 148384–148408. 36 indexed citations

Ahmad, Parvez, Hai Jin, Saqib Qamar, Ran Zheng, & Adnan Saeed. (2021). RD2A: densely connected residual networks using ASPP for brain tumor segmentation. Multimedia Tools and Applications. 80(18). 27069–27094. 16 indexed citations

10.

Qamar, Saqib, Parvez Ahmad, & Linlin Shen. (2021). Dense Encoder-Decoder–Based Architecture for Skin Lesion Segmentation. Cognitive Computation. 13(2). 583–594. 40 indexed citations

11.

Ahmad, Parvez, et al.. (2020). Combined 3D CNN for Brain Tumor Segmentation. abs 1608 6993. 109–112. 3 indexed citations

12.

Qamar, Saqib, et al.. (2019). Hybrid loss guided densely connected convolutional neural network for Ischemic Stroke Lesion segmentation. 1–5. 2 indexed citations

13.

Qamar, Saqib, Hai Jin, Ran Zheng, & Parvez Ahmad. (2019). Multi stream 3D hyper-densely connected network for multi modality isointense infant brain MRI segmentation. Multimedia Tools and Applications. 78(18). 25807–25828. 15 indexed citations

14.

Usama, Mohd, Belal Ahmad, Jun Yang, et al.. (2019). REMOVED: Equipping recurrent neural network with CNN-style attention mechanisms for sentiment analysis of network reviews. Computer Communications. 148. 98–98. 5 indexed citations

15.

Usama, Mohd, Belal Ahmad, Jun Yang, et al.. (2019). Removal notice to “Equipping recurrent neural network with CNN-style attention mechanisms for sentiment analysis of network reviews” [Comput. Commun. 148 (2019) 98–106]. Computer Communications. 148. 250–250.

16.

Qamar, Saqib, Hai Jin, Ran Zheng, Parvez Ahmad, & Mohd Usama. (2019). A variant form of 3D-UNet for infant brain segmentation. Future Generation Computer Systems. 108. 613–623. 84 indexed citations

17.

Qamar, Saqib, Hai Jin, Ran Zheng, & Parvez Ahmad. (2018). 3D Hyper-Dense Connected Convolutional Neural Network for Brain Tumor Segmentation. ResearchSpace (University of Auckland). 123–130. 14 indexed citations

18.

Cai, Shuqin, et al.. (2016). Examining Success of Land Record Information Systems (LRMIS) in Pakistan: Validating an incorporated IS success model. European Scientific Journal ESJ. 12(2). 258–258. 4 indexed citations

19.

Ahmad, Parvez, et al.. (2015). Techniques of Data Mining In Healthcare: A Review. International Journal of Computer Applications. 120(15). 38–50. 53 indexed citations

20.

Qamar, Saqib & Parvez Ahmad. (2015). Emotion Detection from Text using Fuzzy Logic. International Journal of Computer Applications. 121(3). 29–32. 4 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.

Explore authors with similar magnitude of impact