World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
4828
World Ranking
10901
National Ranking
4533

Fahmi Khalifa 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 Fahmi Khalifa 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: 149 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

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

Fahmi Khalifa 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 Fahmi Khalifa 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

Fahmi Khalifa is affiliated with Morgan State University in the United States and has made contributions primarily in the fields of Medicine and Computer Science. Their research extensively covers subfields such as Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, and Ophthalmology.

Their work spans numerous topics, notably Radiomics and Machine Learning in Medical Imaging, AI in cancer detection, and Retinal Imaging and Analysis. Other key topics include Advanced Neural Network Applications, Brain Tumor Detection and Classification, as well as Chaos-based Image/Signal Encryption and Cryptographic Implementations and Security.

Fahmi Khalifa has published research in several venues. The most frequent publication outlets include:

  • Scientific Reports
  • IEEE Access
  • Sensors
  • Bioengineering
  • Algorithms

Collaboration is an important aspect of their research profile, with frequent co-authors including:

  • Ayman El-Baz
  • Mohammed Ghazal
  • Hisham Abdeltawab
  • Harpal S. Sandhu
  • Ahmed Elnakib

Among their notable recent papers are:

  • "An MRI-based deep learning approach for accurate detection of Alzheimer's disease," 2022, Alexandria Engineering Journal
  • "A New Image Encryption Scheme Based on Hybrid Chaotic Maps," 2020, Complexity
  • "A Chaotic-Based Encryption/Decryption Framework for Secure Multimedia Communications," 2020, Entropy
  • "A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images," 2020, Computerized Medical Imaging and Graphics
  • "A Robust Chaos-Based Technique for Medical Image Encryption," 2021, IEEE Access

Their research includes significant contributions in applying deep learning to medical imaging and in development of chaos-based encryption techniques for medical and multimedia signals.

Best Publications

  • Models and methods for analyzing DCE-MRI: A review

    Fahmi Khalifa;Ahmed Soliman;Ayman El-Baz;Mohamed Abou El-Ghar

  • An MRI-based deep learning approach for accurate detection of Alzheimer’s disease

    Unknown

  • Precise Segmentation of 3-D Magnetic Resonance Angiography

    Ayman El-Baz;A. Elnakib;F. Khalifa;Mohamed Abou El-Ghar

  • 3D shape analysis for early diagnosis of malignant lung nodules

    Ayman El-Baz;Matthew Nitzken;Fahmi Khalifa;Ahmed Elnakib

  • Accurate Lungs Segmentation on CT Chest Images by Adaptive Appearance-Guided Shape Modeling

    Ahmed Soliman;Fahmi Khalifa;Ahmed Elnakib;Mohamed Abou El-Ghar

  • Accurate Automatic Analysis of Cardiac Cine Images

    F. Khalifa;G. M. Beache;G. Gimelrfarb;G. A. Giridharan

  • Dynamic Contrast-Enhanced MRI-Based Early Detection of Acute Renal Transplant Rejection

    Fahmi Khalifa;Garth M. Beache;Mohamed Abou El-Ghar;Tarek El-Diasty

  • 3d kidney segmentation from CT images using a level set approach guided by a novel stochastic speed function

    Fahmi Khalifa;Ahmed Elnakib;Garth M. Beache;Georgy Gimel'farb

  • Non-invasive image-based approach for early detection of acute renal rejection

    Fahmi Khalifa;Ayman El-Baz;Georgy Gimel'farb;Mohammed Abu El-Ghar

  • Infant Brain Extraction in T1-Weighted MR Images Using BET and Refinement Using LCDG and MGRF Models

    Amir Alansary;Marwa Ismail;Ahmed Soliman;Fahmi Khalifa

  • 3D shape analysis for early diagnosis of malignant lung nodules

    Ayman El-Baz;Matthew Nitzken;Ahmed Elnakib;Fahmi Khalifa

  • A comprehensive non‐invasive framework for automated evaluation of acute renal transplant rejection using DCE‐MRI

    Fahmi Khalifa;Mohamed Abou El-Ghar;Behnaz Abdollahi;Hermann B. Frieboes

  • A New Image Encryption Scheme Based on Hybrid Chaotic Maps

    Ibrahim Yasser;Fahmi Khalifa;Mohamed A. Mohamed;Ahmed Shaban Samrah

  • A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images.

    Hisham Abdeltawab;Fahmi Khalifa;Fatma Taher;Norah Saleh Alghamdi

  • A Chaotic-Based Encryption/Decryption Framework for Secure Multimedia Communications.

    Ibrahim Yasser;Mohamed A. Mohamed;Ahmed S. Samra;Fahmi Khalifa

  • Shape-Appearance Guided Level-Set Deformable Model for Image Segmentation

    Fahmi Khalifa;Ayman El-Baz;Georgy Gimel'farb;Rosemary Ouseph

  • A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction.

    Hisham Abdeltawab;Mohamed Shehata;Ahmed Shalaby;Fahmi Khalifa

  • State-of-the-Art Medical Image Registration Methodologies: A Survey

    Fahmi Khalifa;Garth M. Beache;Georgy Gimel’farb;Jasjit S. Suri

  • A new deformable model-based segmentation approach for accurate extraction of the kidney from abdominal CT images

    F. Khalifa;G. Gimel'farb;M. Abo El-Ghar;G. Sokhadze

  • Myocardial borders segmentation from cine MR images using bidirectional coupled parametric deformable models.

    Hisham Sliman;Fahmi Khalifa;Ahmed Elnakib;Ahmed Soliman

  • A level set-based framework for 3D kidney segmentation from diffusion MR images

    Mohamed Shehata;Fahmi Khalifa;Ahmed Soliman;Rahaf Alrefai

  • Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

    Mohamed Shehata;Fahmi Khalifa;Ahmed Soliman;Mohammed Ghazal

Frequent Co-Authors

Ayman El-Baz
Ayman El-Baz University of Louisville
Georgy Gimel'farb
Georgy Gimel'farb University of Auckland
Manuel F. Casanova
Manuel F. Casanova University of South Carolina
Jasjit S. Suri
Jasjit S. Suri University of Idaho
Mohamed A. Mohamed
Mohamed A. Mohamed Minia University
Liang Cheng
Liang Cheng Brown University
Jacek M. Zurada
Jacek M. Zurada University of Louisville
Ahmed M. Soliman
Ahmed M. Soliman Cairo University
Mohamed E. Mahmoud
Mohamed E. Mahmoud University of Alabama
Johanna M. Seddon
Johanna M. Seddon University of Massachusetts Chan Medical School

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