World's Best Scientists 2026 revealed!
Pedro Pedrosa Rebouças Filho

Pedro Pedrosa Rebouças Filho

D-Index & Metrics

Computer Science

D-Index
37
Citations
5046
World Ranking
10868
National Ranking
36

Pedro Pedrosa Rebouças Filho 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 Pedro Pedrosa Rebouças Filho 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: 159 publications — 30th percentile

30% of scientists in this discipline score the same or lower.

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

Pedro Pedrosa Rebouças Filho 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 Pedro Pedrosa Rebouças Filho 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Pedro Pedrosa Rebouças Filho is affiliated with the Instituto Federal do Ceará in Brazil. Their research activity spans multiple disciplines, including Medicine, Computer Science, and Engineering. The scientist's work is particularly concentrated in subfields such as Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Biomedical Engineering, and Pulmonary and Respiratory Medicine.

The main research topics addressed include COVID-19 diagnosis using AI, Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, Lung Cancer Diagnosis and Treatment, Brain Tumor Detection and Classification, Advanced Neural Network Applications, and Medical Image Segmentation Techniques.

Pedro Pedrosa Rebouças Filho has published in various scientific journals and conferences, with frequent publications in venues such as:

  • Congresso Brasileiro de Automática
  • Learning and Nonlinear Models
  • Pattern Recognition Letters
  • IEEE Access
  • IEEE Sensors Journal

Collaborations with other researchers are a notable part of their work. Frequent co-authors include:

  • Victor Hugo C. de Albuquerque (19 co-authored works)
  • Luís Fabrício de Freitas Souza (14 co-authored works)
  • Adriell Gomes Marques (12 co-authored works)
  • Elene Firmeza Ohata (11 co-authored works)
  • Francisco H. S. Silva (9 co-authored works)

Recent publications from Pedro Pedrosa Rebouças Filho highlight their contributions to medical imaging and artificial intelligence applied to healthcare challenges. Selected recent papers include:

  • "Automatic detection of COVID-19 infection using chest X-ray images through transfer learning" (2020), IEEE/CAA Journal of Automatica Sinica
  • "A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system" (2020), Pattern Recognition Letters
  • "An effective approach for CT lung segmentation using mask region-based convolutional neural networks" (2020), Artificial Intelligence in Medicine
  • "An Open IoHT-Based Deep Learning Framework for Online Medical Image Recognition" (2020), IEEE Journal on Selected Areas in Communications
  • "Computation Offloading for Vehicular Environments: A Survey" (2020), IEEE Access

Best Publications

  • Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications

    Tiago Carneiro;Raul Victor Medeiros Da Nobrega;Thiago Nepomuceno;Gui-Bin Bian

  • Detecting Parkinson's Disease with Sustained Phonation and Speech Signals using Machine Learning Techniques

    Jefferson Almeida;Pedro Pedrosa Rebouças Filho;Tiago Carneiro;Wei Wei

  • Automatic detection of COVID-19 infection using chest X-ray images through transfer learning

    Elene Firmeza Ohata;Gabriel Maia Bezerra;Joao Victor Souza das Chagas;Aloisio Vieira Lira Neto

  • Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook

    Marcus A. G. Santos;Roberto Muñoz;Rodrigo Olivares;Pedro Pedrosa Rebouças Filho

  • A novel electrocardiogram feature extraction approach for cardiac arrhythmia classification

    Leandro Bezerra Marinho;Navar de Medeiros Mendonça e Nascimento;João Wellington M. Souza;Mateus Valentim Gurgel

  • A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system

    Douglas de A. Rodrigues;Roberto F. Ivo;Suresh Chandra Satapathy;Shuihua Wang

  • Lung Nodule Classification via Deep Transfer Learning in CT Lung Images

    Raul Victor Medeiros da Nobrega;Solon Alves Peixoto;Suane Pires P. da Silva;Pedro Pedrosa Reboucas Filho

  • Deep learning IoT system for online stroke detection in skull computed tomography images

    Carlos M.J.M. Dourado;Suane Pires P. da Silva;Raul Victor M. da Nóbrega;Antonio Carlos da S. Barros

  • Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images.

    Pedro Pedrosa Rebouças Filho;Paulo César Cortez;Antônio Carlos da Silva Barros;Victor Hugo C. de Albuquerque

  • An effective approach for CT lung segmentation using mask region-based convolutional neural networks.

    Qinhua Hu;Luís Fabrício de F. Souza;Gabriel Bandeira Holanda;Shara S. A. Alves

  • Health of Things Algorithms for Malignancy Level Classification of Lung Nodules

    Murillo B. Rodrigues;Raul Victor M. Da Nobrega;Shara Shami A. Alves;Pedro Pedrosa Reboucas Filho

  • An Open IoHT-Based Deep Learning Framework for Online Medical Image Recognition

    Carlos M. J. M. Dourado;Suane Pires P. da Silva;Raul Victor M. da Nobrega;Pedro P. Reboucas Filho

  • Computation Offloading for Vehicular Environments: A Survey

    Alisson Barbosa De Souza;Paulo A. L. Rego;Tiago Carneiro;Jardel Das C. Rodrigues

  • Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks

    Raul Victor Medeiros da Nóbrega;Pedro Pedrosa Rebouças Filho;Murillo Barata Rodrigues;Suane Pires Pinheiro da Silva

  • A novel transfer learning approach for the classification of histological images of colorectal cancer

    Elene Firmeza Ohata;João Victor Souza das Chagas;Gabriel Maia Bezerra;Mohammad Mehedi Hassan

  • New approach to detect and classify stroke in skull CT images via analysis of brain tissue densities.

    Pedro Pedrosa Rebouças Filho;Róger M. Sarmento;Gabriel Bandeira Holanda;Daniel de Alencar Lima

  • Rotation-invariant feature extraction using a structural co-occurrence matrix

    Geraldo L. Bezerra Ramalho;Daniel S. Ferreira;Pedro P. Rebouças Filho;Fátima N. Sombra de Medeiros

  • Evolutionary algorithms for automatic lung disease detection

    Naman Gupta;Deepak Gupta;Ashish Khanna;Pedro P. Rebouças Filho

  • Novel Adaptive Balloon Active Contour Method based on internal force for image segmentation - A systematic evaluation on synthetic and real images

    Pedro Pedrosa Rebouças Filho;Paulo César Cortez;Antônio Carlos Da Silva Barros;Victor Hugo C. De Albuquerque

  • Deep Learning-Enhanced Internet of Medical Things to Analyze Brain CT Scans of Hemorrhagic Stroke Patients: A New Approach

    Yongzhao Xu;Gabriel Holanda;Luis Fabricio. de F. Souza;Hercules Silva

  • Classification of EEG signals to detect alcoholism using machine learning techniques

    Jardel das C. Rodrigues;Pedro P. Rebouças Filho;Eugenio Peixoto;Arun Kumar N

  • Brinell and Vickers Hardness Measurement Using Image Processing and Analysis Techniques

    Pedro Pedrosa Rebouças Filho;Tarique da Silveira Cavalcante;Victor Hugo Costa de Albuquerque;João Manuel Ribeiro Silva Tavares

Frequent Co-Authors

Victor Hugo C. de Albuquerque
Victor Hugo C. de Albuquerque Universidade Federal do Ceará
João Manuel R. S. Tavares
João Manuel R. S. Tavares University of Porto
Mohammad Mehedi Hassan
Mohammad Mehedi Hassan King Saud University
João Paulo Papa
João Paulo Papa Sao Paulo State University
Arun Kumar Sangaiah
Arun Kumar Sangaiah National Yunlin University of Science and Technology
Joel J. P. C. Rodrigues
Joel J. P. C. Rodrigues Federal University of Piauí
Sidarta Ribeiro
Sidarta Ribeiro Federal University of Rio Grande do Norte
Biplab Sikdar
Biplab Sikdar National University of Singapore
Wei Wei
Wei Wei Qilu University of Technology
Vinay Chamola
Vinay Chamola Birla Institute of Technology and Science, Pilani

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