Michael Tang

416 total citations
20 papers, 242 citations indexed

About

Michael Tang is a scholar working on Radiology, Nuclear Medicine and Imaging, Education and Ophthalmology. According to data from OpenAlex, Michael Tang has authored 20 papers receiving a total of 242 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Education and 4 papers in Ophthalmology. Recurrent topics in Michael Tang's work include Retinal Diseases and Treatments (4 papers), Retinal Imaging and Analysis (4 papers) and Retinal and Optic Conditions (3 papers). Michael Tang is often cited by papers focused on Retinal Diseases and Treatments (4 papers), Retinal Imaging and Analysis (4 papers) and Retinal and Optic Conditions (3 papers). Michael Tang collaborates with scholars based in United States, Malaysia and Cameroon. Michael Tang's co-authors include Soo Siang Teoh, Haidi Ibrahim, Embong Zunaina, Michael J. Jacobson, Paul Ginns, Savvas Damaskinos, Corwyn Rowsell, Mahdi S. Hosseini, Jun Deng and Konstantinos N. Plataniotis and has published in prestigious journals such as IEEE Access, Sensors and Educational Psychology Review.

In The Last Decade

Michael Tang

17 papers receiving 224 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael Tang United States	9	103	85	54	53	41	20	242
Mark Stimson United States	4	8 0.1×	68 0.8×	33 0.6×	11 0.2×	18 0.4×	6	324
Norman Bier United States	6	21 0.2×	9 0.1×	106 2.0×	15 0.3×	86 2.1×	15	277
Maison Maison Indonesia	19	15 0.1×	15 0.2×	16 0.3×	6 0.1×	1.0k 25.0×	126	1.1k
Di Xu China	10	7 0.1×	99 1.2×	8 0.1×	1 0.0×	76 1.9×	35	256
Areej ElSayary United Arab Emirates	8	11 0.1×	23 0.3×	60 1.1×		105 2.6×	38	288
Paul Michel United States	8	4 0.0×	29 0.3×	135 2.5×	5 0.1×	13 0.3×	23	217
Min Lun Wu United States	8	8 0.1×	81 1.0×	18 0.3×		78 1.9×	27	267
Amr M. Mohamed Saudi Arabia	6	16 0.2×	12 0.1×	154 2.9×		75 1.8×	21	349
Jasiya Jabbar Oman	10	9 0.1×	18 0.2×	50 0.9×		129 3.1×	18	254
Reza Hadi Mogavi Canada	7	15 0.1×	10 0.1×	93 1.7×		21 0.5×	18	247

Countries citing papers authored by Michael Tang

Since Specialization

Citations

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

Fields of papers citing papers by Michael Tang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Tang. A scholar is included among the top collaborators of Michael Tang 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 Michael Tang. Michael Tang 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

López, Dolores E., Michael Tang, Nathan Lee, & Mohammad Husain. (2025). UAV/Drone Detection and Classification Using Radio Frequency Machine Learning. 1–10.

Tang, Michael, et al.. (2024). Enhanced Brain Tumor Classification using Modified ResNet50 Architecture. 6 indexed citations

Tang, Michael, et al.. (2024). DenseNet201-Based Waste Material Classification Using Transfer Learning Approach. 13(2). 113–120. 5 indexed citations

Tang, Michael & Soo Siang Teoh. (2023). Brain Tumor Detection from MRI Images Based on ResNet18. 1–5. 10 indexed citations

Tang, Michael, Soo Siang Teoh, & Haidi Ibrahim. (2022). Retinal Vessel Segmentation from Fundus Images Using DeepLabv3+. 377–381. 16 indexed citations

Tang, Michael, Soo Siang Teoh, Haidi Ibrahim, & Embong Zunaina. (2022). A Deep Learning Approach for the Detection of Neovascularization in Fundus Images Using Transfer Learning. IEEE Access. 10. 20247–20258. 34 indexed citations

Tang, Michael, Soo Siang Teoh, Haidi Ibrahim, & Embong Zunaina. (2021). Neovascularization Detection and Localization in Fundus Images Using Deep Learning. Sensors. 21(16). 5327–5327. 31 indexed citations

Tang, Michael & Soo Siang Teoh. (2020). Blood Vessel Segmentation in Fundus Images Using Hessian Matrix for Diabetic Retinopathy Detection. 728–733. 13 indexed citations

Tang, Michael, et al.. (2020). Integration of Science, Technology, and Society (STS) Courses into the Engineering Curriculum. 25.821.1–25.821.14.

10.

Mondal, Mainack, et al.. (2019). Moving Beyond Set-It-And-Forget-It Privacy Settings on Social Media. 991–1008. 19 indexed citations

11.

Hosseini, Mahdi S., Michael Tang, Jun Deng, et al.. (2019). Atlas of Digital Pathology: A Generalized Hierarchical Histological Tissue Type-Annotated Database for Deep Learning. 11739–11748. 35 indexed citations

12.

Tang, Michael, Paul Ginns, & Michael J. Jacobson. (2019). Tracing Enhances Recall and Transfer of Knowledge of the Water Cycle. Educational Psychology Review. 31(2). 439–455. 25 indexed citations

13.

Tang, Michael. (2015). The impact of science fiction media on student interest and learning. Montana State University ScholarWorks (Montana State University). 2 indexed citations

14.

Tang, Michael, et al.. (2013). eLmaps: An Educational Software Application Based on Principles of Cognitive Linguistics and Cognitive Science. The International Journal of Technology Knowledge and Society. 8(5). 49–64.

15.

Tang, Michael, et al.. (2012). A Mathematical Analysis of Semantic Maps, with Theoretical and Applied Implications for Blended Learning Software.. International journal on e-learning. 11(1). 95–104. 1 indexed citations

16.

Tang, Michael, et al.. (2007). University anti-plagiarism efforts versus commercial anti-plagiarism software and services and do online students cheat more?. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2007(1). 6595–6601. 3 indexed citations

17.

Tang, Michael, et al.. (2007). Regular Versus Online Versus Blended: A Qualitative Description of the Advantages of the Electronic Modes and a Quantitative Evaluation. International journal on e-learning. 6(2). 257–266. 34 indexed citations

18.

Johnson, Lynn E. & Michael Tang. (2005). Integration of e-Management, e-Development and e-Learning Technologies for Blended Course Delivery.. 13(2). 185–199. 4 indexed citations

19.

Tang, Michael, et al.. (2005). Regular vs. online vs. blended: a qualitative description of the advantages of the electronic modes and a quantitative evaluation. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2005(1). 442–448. 2 indexed citations

20.

Tang, Michael & Lynn E. Johnson. (1999). Changing the Learning Paradigm through the STC-Link Electronic Archive. Journal of Professional Issues in Engineering Education and Practice. 125(1). 19–24. 2 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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