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Topic · Neurology

Brain Tumor Detection and Classification

Brain Tumor Detection and Classification is a research topic within Neurology. Science Explorer counts 26k research works in it since 1953. 15.4% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the classification of brain tumor type and grade using various techniques such as MRI, deep learning, convolutional neural networks, feature extraction, and machine learning. The research aims to improve the accuracy and efficiency of brain tumor classification for better diagnosis and treatment.

  • MRI
  • Brain Tumor
  • Classification
  • Deep Learning
  • Convolutional Neural Network
  • Feature Extraction
  • Machine Learning
  • Image Segmentation
  • Transfer Learning
  • Medical Imaging
Research works
26k
fractional, since 1953
In the world top 10%
3.9k
per year above
Top-10% rate
15.4%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+222%
the tick is no change

Which countries lead Brain Tumor Detection and Classification research?

By volume, India and China publish the most (5k and 3.3k works in 2022–2025).

By volume, 2022–2025

  1. 1 India 5k works
  2. 2 China 3.3k works
  3. 3 United States 1.1k works
  4. 4 Indonesia 388 works
  5. 5 ?? 307 works
  6. 6 Bangladesh 262 works
  7. 7 Saudi Arabia 262 works
  8. 8 Türkiye 252 works
  9. 9 South Korea 245 works
  10. 10 United Kingdom 244 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: 44.0%China: 29.1%United States: 9.7%Indonesia: 3.4%6 others listed: 13.8%44%largest
India5,007 · 44.0%China3,312 · 29.1%United States1,100 · 9.7%Indonesia388 · 3.4%6 others listed1,572 · 13.8%

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

Which institutions lead Brain Tumor Detection and Classification research?

By volume in 2022–2025, Saveetha University publishes the most Brain Tumor Detection and Classification research, followed by SRM Institute of Science and Technology and Vellore Institute of Technology University.

Who are the leading researchers in Brain Tumor Detection and Classification?

The most-cited researchers publishing on Brain Tumor Detection and Classification include Xiangyu Zhang, John C. Morris and Luca Benini.

  1. 1 Xiangyu Zhang 6.9k citations
  2. 2 John C. Morris United States 5.1k citations
  3. 3 Luca Benini Italy 4.6k citations
  4. 4 Dusit Niyato Singapore 4.5k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Brain Tumor Detection and Classification research done?

The largest centres of Brain Tumor Detection and Classification research in 2022–2025 are Chennai (India), Beijing (China), Coimbatore (India) and Bengaluru (India). Among places with at least 20 works in it, it is an unusually large share of all research in Guntur, Srivilliputhur and Dindigul.

Largest cities, 2022–2025

  1. 1 Chennai India 848 works
  2. 2 Beijing China 473 works
  3. 3 Coimbatore India 254 works
  4. 4 Bengaluru India 244 works
  5. 5 Shanghai China 240 works
  6. 6 Vellore India 172 works
  7. 7 Pune India 163 works
  8. 8 Hyderabad India 161 works
  9. 9 Dhaka Bangladesh 160 works
  10. 10 Nanjing China 149 works

Where it is the local speciality

  1. Guntur28.0 works27×
  2. SrivilliputhurIN · 59.1 works27×
  3. DindigulIN · 29.1 works26×
  4. Coimbatore26.8 works22×
  5. Bengaluru20.9 works20×
  6. VijayawadaIN · 132.7 works20×
← less than its size predictsmore →

Location quotient: how much more of its research is in Brain Tumor Detection and Classification than the world average.

See Brain Tumor Detection and Classification on the map

Where is the best place to study Brain Tumor Detection and Classification?

Among universities, judged by research, Vellore Institute of Technology University, Saveetha University and University of Aizu 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%60%mean 23.55%fractional works in this node (log) →share in the world top 10% →Vellore Institute of Technology University: 169, 21.9%Saveetha University: 224, 14.9%University of Aizu: 8, 49.6%Kalasalingam Academy of Research and Education: 59, 12.6%COMSATS University Islamabad: 13, 48.8%Princess Nourah bint Abdulrahman University: 20, 47.4%Sathyabama Institute of Science and Technology: 53, 7.3%Chitkara University: 124, 13.8%SRM Institute of Science and Technology: 201, 8.7%Annamalai University: 33, 10.5%University of AizuVellore Institute of…Saveetha UniversityKalasalingam Academy…
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 70.921.9%12.0×169 +702.4%
2 Saveetha UniversityIndia 61.414.9%16.4×224
3 University of AizuJapan 60.949.6%13.6×8
4 Kalasalingam Academy of Research and EducationIndia 59.912.6%26.7×59 +629.4%
5 COMSATS University IslamabadPakistan 59.748.8%3.8×13 +356.0%
6 Princess Nourah bint Abdulrahman UniversitySaudi Arabia 58.647.4%5.3×20
7 Sathyabama Institute of Science and TechnologyIndia 56.77.3%17.7×53 +538.5%
8 Chitkara UniversityIndia 56.613.8%22.5×124
9 SRM Institute of Science and TechnologyIndia 56.28.7%16.8×201
10 Annamalai UniversityIndia 54.910.5%11.7×33 +212.5%

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 Brain Tumor Detection and Classification research growing?

Output in 2018–2022 was 222% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Brain Tumor Detection and Classification.

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.