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Topic · Computer Vision and Pattern Recognition

Digital Imaging for Blood Diseases

Digital Imaging for Blood Diseases is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 16k research works in it since 1950. 17.6% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the automated analysis of blood cell images, particularly in the context of detecting malaria parasites and classifying leukemia. The research utilizes techniques such as image processing, convolutional neural networks, and machine learning for tasks including white blood cell segmentation, feature extraction, and automated diagnosis from microscopic blood images.

  • Malaria Parasite Detection
  • Leukemia Classification
  • Image Processing
  • Convolutional Neural Networks
  • White Blood Cell Segmentation
  • Automated Diagnosis
  • Microscopic Blood Images
  • Machine Learning
  • Feature Extraction
  • Medical Image Analysis
Research works
16k
fractional, since 1950
In the world top 10%
2.9k
per year above
Top-10% rate
17.6%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+153%
the tick is no change

Which countries lead Digital Imaging for Blood Diseases research?

By volume, India and China publish the most (1.8k and 863 works in 2022–2025).

By volume, 2022–2025

  1. 1 India 1.8k works
  2. 2 China 863 works
  3. 3 United States 504 works
  4. 4 Indonesia 392 works
  5. 5 Türkiye 158 works
  6. 6 Brazil 142 works
  7. 7 Pakistan 136 works
  8. 8 Bangladesh 122 works
  9. 9 United Kingdom 120 works
  10. 10 Germany 109 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

India: 40.9%China: 20.0%United States: 11.7%Indonesia: 9.1%6 others listed: 18.3%41%largest
India1,762 · 40.9%China863 · 20.0%United States504 · 11.7%Indonesia392 · 9.1%6 others listed788 · 18.3%

Shares of the rows listed above, not of the whole node.

Which institutions lead Digital Imaging for Blood Diseases research?

By volume in 2022–2025, Saveetha University publishes the most Digital Imaging for Blood Diseases research, followed by Vellore Institute of Technology University and SRM Institute of Science and Technology.

By volume, 2022–2025

  1. 1 Saveetha UniversityIndia 95 works
  2. 2 Vellore Institute of Technology UniversityIndia 54 works
  3. 3 SRM Institute of Science and TechnologyIndia 54 works
  4. 4 Chitkara UniversityIndia 50 works
  5. 5 Amrita Vishwa VidyapeethamIndia 30 works
  6. 6 Graphic Era UniversityIndia 27 works
  7. 7 Koneru Lakshmaiah Education FoundationIndia 27 works
  8. 8 Chandigarh UniversityIndia 24 works
  9. 9 Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and TechnologyIndia 19 works
  10. 10 Manipal Academy of Higher EducationIndia 17 works

Where is Digital Imaging for Blood Diseases research done?

The largest centres of Digital Imaging for Blood Diseases research in 2022–2025 are Chennai (India), Beijing (China), Coimbatore (India) and Dhaka (Bangladesh). Among places with at least 20 works in it, it is an unusually large share of all research in Vijayawada, Chandigarh and Chennai.

Largest cities, 2022–2025

  1. 1 Chennai India 276 works
  2. 2 Beijing China 117 works
  3. 3 Coimbatore India 86 works
  4. 4 Dhaka Bangladesh 80 works
  5. 5 Shanghai China 72 works
  6. 6 Bengaluru India 72 works
  7. 7 New Delhi India 71 works
  8. 8 Chandigarh India 61 works
  9. 9 Pune India 61 works
  10. 10 Vellore India 55 works

Where it is the local speciality

  1. VijayawadaIN · 40.6 works15×
  2. ChandigarhIN · 61.4 works9.6×
  3. ChennaiIN · 275.8 works9.5×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Digital Imaging for Blood Diseases than the world average.

See Digital Imaging for Blood Diseases on the map

Where is the best place to study Digital Imaging for Blood Diseases?

Among universities, judged by research, Vellore Institute of Technology University, Chitkara University and Saveetha University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 24.6%fractional works in this node (log) →share in the world top 10% →Vellore Institute of Technology University: 54, 22.2%Chitkara University: 50, 27.3%Saveetha University: 95, 11.8%Daffodil International University: 12, 25.9%Manipal Academy of Higher Education: 18, 29.9%Graphic Era University: 27, 18.0%Amrita Vishwa Vidyapeetham: 30, 20.3%Jadavpur University: 11, 27.2%GLA University: 8, 35.1%VIT-AP University: 9, 28.3%Chitkara UniversityDaffodil Internation…Vellore Institute of…Saveetha University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Vellore Institute of Technology UniversityIndia 69.822.2%9.1×54 +236.7%
2 Chitkara UniversityIndia 67.327.3%21.5×50 —
3 Saveetha UniversityIndia 62.811.8%16.4×95 —
4 Daffodil International UniversityBangladesh 60.225.9%22.2×12 —
5 Manipal Academy of Higher EducationIndia 58.429.9%6.0×18 +554.2%
6 Graphic Era UniversityIndia 56.418.0%13.2×27 —
7 Amrita Vishwa VidyapeethamIndia 55.620.3%9.4×30 —
8 Jadavpur UniversityIndia 54.627.2%6.9×11 +212.3%
9 GLA UniversityIndia 53.235.1%7.7×8 —
10VIT-AP University?? 53.128.3%21.0×9 —

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Digital Imaging for Blood Diseases research growing?

Output in 2018–2022 was 153% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Digital Imaging for Blood Diseases.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.