World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
15296
World Ranking
4483
National Ranking
2097

Heng-Da Cheng publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Heng-Da Cheng sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 256 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Heng-Da Cheng D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Heng-Da Cheng sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Heng-Da Cheng is affiliated with Utah State University in the United States. Their research spans multiple disciplines including Engineering, Computer Science, and Medicine, demonstrating a cross-disciplinary approach that integrates computational methods with practical applications.

Their major fields of study include:

  • Engineering
  • Computer Science
  • Medicine

The subfields Cheng frequently contributes to are:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Radiology, Nuclear Medicine and Imaging
  • Mechanical Engineering

The scientist's work covers several main research topics, such as:

  • AI in cancer detection
  • Radiomics and Machine Learning in Medical Imaging
  • Medical Image Segmentation Techniques
  • Building Energy and Comfort Optimization
  • Energy Load and Power Forecasting
  • Infrastructure Maintenance and Monitoring
  • Smart Grid Energy Management

Heng-Da Cheng has published in a variety of academic venues, with frequent publications appearing in:

  • Energy and Buildings
  • Healthcare
  • SSRN Electronic Journal
  • IEEE Transactions on Intelligent Transportation Systems
  • Applied Intelligence

Several recent papers highlight Cheng's research contributions:

  • Ensemble 1-D CNN diagnosis model for VRF system refrigerant charge faults under heating condition, 2020, Energy and Buildings
  • CrackGAN: Pavement Crack Detection Using Partially Accurate Ground Truths Based on Generative Adversarial Learning, 2020, IEEE Transactions on Intelligent Transportation Systems
  • SMOTE-WENN: Solving class imbalance and small sample problems by oversampling and distance scaling, 2020, Applied Intelligence
  • Self-Supervised Structure Learning for Crack Detection Based on Cycle-Consistent Generative Adversarial Networks, 2020, Journal of Computing in Civil Engineering
  • BUSIS: A Benchmark for Breast Ultrasound Image Segmentation, 2022, Healthcare

Cheng's frequent collaborators include:

  • Yingtao Zhang
  • Huanxin Chen
  • Min Xian
  • Shouhua Luo
  • Kuan Huang

Best Publications

  • Color image segmentation: advances and prospects

    Heng-Da Cheng;Xihua Jiang;Ying Sun;Jingli Wang

  • Automated breast cancer detection and classification using ultrasound images: A survey

    H. D. Cheng;Juan Shan;Wen Ju;Yanhui Guo

  • Computer-aided detection and classification of microcalcifications in mammograms: a survey

    Heng-Da Cheng;Xiaopeng Cai;Xiaowei Chen;Liming Hu

  • Approaches for automated detection and classification of masses in mammograms

    H. D. Cheng;X. J. Shi;R. Min;L. M. Hu

  • A simple and effective histogram equalization approach to image enhancement

    Heng-Da Cheng;X. J. Shi

  • A hierarchical approach to color image segmentation using homogeneity

    Heng-Da Cheng;Ying Sun

  • Threshold selection based on fuzzy c-partition entropy approach

    Heng-Da Cheng;Jim-Rong Chen;Jiguang Li

  • A novel approach to microcalcification detection using fuzzy logic technique

    Heng-Da Cheng;Yui Man Lui;R.I. Freimanis

  • New neutrosophic approach to image segmentation

    Yanhui Guo;H. D. Cheng

  • Novel Approach to Pavement Cracking Detection Based on Fuzzy Set Theory

    H. D. Cheng;Jim-Rong Chen;Chris Glazier;Y. G. Hu

  • Automatic Breast Ultrasound Image Segmentation: A Survey

    Min Xian;Yingtao Zhang;Heng-Da Cheng;Heng-Da Cheng;Fei Xu

  • Color image segmentation based on homogram thresholding and region merging

    Heng-Da Cheng;Xihua Jiang;Jingli Wang

  • A novel fuzzy logic approach to contrast enhancement

    Heng-Da Cheng;Hui juan Xu

  • A novel fuzzy entropy approach to image enhancement and thresholding

    H. D. Cheng;Yen-Hung Cheng;Ying Sun

  • Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning

    Kaige Zhang;H. D. Cheng;Boyu Zhang

  • A neutrosophic approach to image segmentation based on watershed method

    Ming Zhang;Ling Zhang;H. D. Cheng

  • Thresholding using two-dimensional histogram and fuzzy entropy principle

    H.D. Cheng;Y.H. Chen;X.H. Jiang

  • Automatic pavement distress detection system

    H. D. Cheng;M. Miyojim

  • Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images

    Bo Liu;H. D. Cheng;Jianhua Huang;Jiawei Tian

  • CrackGAN: Pavement Crack Detection Using Partially Accurate Ground Truths Based on Generative Adversarial Learning

    Kaige Zhang;Yingtao Zhang;Heng-Da Cheng

  • A Neutrosophic Approach to Image Segmentation Based on Watershed Method

    Ming Zhang;LingZhang;H.D.Cheng

Frequent Co-Authors

Yanhui Guo
Yanhui Guo Fudan University
Xianglong Tang
Xianglong Tang Harbin Institute of Technology
King-Sun Fu
King-Sun Fu Purdue University West Lafayette
Christopher M. U. Neale
Christopher M. U. Neale University of Nebraska–Lincoln
Jeffrey J. McDonnell
Jeffrey J. McDonnell University of Saskatchewan
Ching Y. Suen
Ching Y. Suen Concordia University
Rutvik H. Desai
Rutvik H. Desai University of South Carolina
Yuan Yan Tang
Yuan Yan Tang University of Macau
Yao Zhao
Yao Zhao Beijing Jiaotong University
YangQuan Chen
YangQuan Chen University of California, Merced

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