Pin Wang

3.5k citations
170 papers · 2.3k indexed · 1 hit paper · h-index 21

Impact in

Papers in

Pin Wang

150 papers receiving 2.2k citations

Hit Papers

Comparative analysis of image classification algorithms based on traditional machine learning and deep learning 2020 · 510 citations
5102020202620222024100200300400500

Peers

Pin Wang
Comparison fields: 5 of 191
  • Computer Vision and Pattern Recognition 559
  • Artificial Intelligence 809
  • Biophysics 113
  • Radiology, Nuclear Medicine and Imaging 429
  • Media Technology 150
Replace M. Iqbal Saripan with:
M. Iqbal Saripan Malaysia
Enmin Song China
Mei Chen China
Agnieszka Mikołajczyk Poland
Saad Albawi Iraq
Pau‐Choo Chung Taiwan
Saad Al-Azawi Iraq
Tareq Abed Mohammed Iraq
Daniele Ravì Italy
Dadong Wang Australia
Pin Wang relative to M. Iqbal Saripan Malaysia M. Iqbal Saripan's profile →
Citations per field
00.5×1.7×
M. Iqbal Saripan · 1×
Citations per year

Countries citing papers authored by Pin Wang

Since Specialization
Citations

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

Fields of papers citing papers by Pin Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Pin Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Pin Wang Line = papers co-authored together Pin Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 170 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Comparative analysis of image classification algorithms based on traditional machine learning and deep learning
Hit paper breakdown →
2020510
2 2015199
3 2020118
4 201893
5 202176
6 201056
7 202048
8 201947
9 201644
10 202044
11 202141
12 202039
13 202031
14 201731
15 201530
16 201930
17 201629
18 202128
19 201526
20 202025

About Pin Wang

Pin Wang is a scholar working on General Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing and Media Technology, having authored 170 papers that have together received 2.3k indexed citations. Recurring topics across this work include AI in cancer detection (13 papers), Voice and Speech Disorders (12 papers), Music and Audio Processing (9 papers), Anomaly Detection Techniques and Applications (8 papers), Speech Recognition and Synthesis (8 papers), Imbalanced Data Classification Techniques (7 papers), Medical Image Segmentation Techniques (7 papers) and Radiomics and Machine Learning in Medical Imaging (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (559 citations), Artificial Intelligence (809 citations), Biophysics (113 citations), Radiology, Nuclear Medicine and Imaging (429 citations) and Media Technology (150 citations). Pin Wang has collaborated with scholars based in China, United States and Sweden. Frequent co-authors include En Fan, Peng Wang, Yongming Li, Xianling Hu, Zhu Xin-jian, Mingfeng Jiang, Qianqian Liu, Jiaxin Wang, Song Qi and Shanshan Lv. Their work appears in journals such as Biomedical Signal Processing and Control, IEEE Access, BioMedical Engineering OnLine, Applied Intelligence and Information Sciences.

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

Rankless by CCL
2026