H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 44 Citations 8,393 145 World Ranking 3771 National Ranking 1922

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

M. Emre Celebi spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Cluster analysis. His Artificial intelligence study incorporates themes from Lesion and Machine learning. His Computer vision research is multidisciplinary, incorporating perspectives in Algorithm and Computer-aided diagnosis.

His study in the fields of Classifier, Feature selection and Support vector machine classification under the domain of Pattern recognition overlaps with other disciplines such as Important research. His biological study spans a wide range of topics, including Domain and Information retrieval. His studies deal with areas such as Initialization and Data mining as well as Cluster analysis.

His most cited work include:

  • A comparative study of efficient initialization methods for the k-means clustering algorithm (610 citations)
  • Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) (595 citations)
  • A Methodological Approach to the Classification of Dermoscopy Images (416 citations)

What are the main themes of his work throughout his whole career to date?

Artificial intelligence, Computer vision, Pattern recognition, Cluster analysis and Image processing are his primary areas of study. His biological study deals with issues like Skin lesion, which deal with fields such as Machine learning. His Computer vision study which covers Computer-aided diagnosis that intersects with Identification.

His work on Classifier as part of general Pattern recognition study is frequently linked to Pigmented skin, therefore connecting diverse disciplines of science. His Cluster analysis research incorporates elements of Initialization, Data mining and Color quantization. In his work, Graphics, Order statistic and Filter is strongly intertwined with Algorithm, which is a subfield of Image processing.

He most often published in these fields:

  • Artificial intelligence (74.58%)
  • Computer vision (48.59%)
  • Pattern recognition (37.29%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (74.58%)
  • Skin lesion (14.69%)
  • Deep learning (3.95%)

In recent papers he was focusing on the following fields of study:

M. Emre Celebi mostly deals with Artificial intelligence, Skin lesion, Deep learning, Computer vision and Skin cancer. His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition. His work carried out in the field of Skin lesion brings together such families of science as Domain, Medical image computing, Set and Medical imaging.

His work in Computer vision tackles topics such as Convolutional neural network which are related to areas like Visualization, Software, Graphics and Image segmentation. His work on Melanoma detection as part of general Skin cancer research is frequently linked to Medical physics, Receiver operating characteristic and Skin imaging, thereby connecting diverse disciplines of science. His Image processing study integrates concerns from other disciplines, such as Initialization, Quantization, Computer graphics and Cluster analysis.

Between 2017 and 2021, his most popular works were:

  • Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) (595 citations)
  • Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC). (131 citations)
  • Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images (128 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer vision

M. Emre Celebi focuses on Skin lesion, Medical imaging, Artificial intelligence, Information retrieval and Segmentation. In his research, Algorithm and Dermatology is intimately related to Skin imaging, which falls under the overarching field of Skin lesion. His study in Artificial intelligence focuses on Deep learning in particular.

His studies in Information retrieval integrate themes in fields like Domain, Medical image computing and Set. Segmentation is a subfield of Computer vision that he studies. His research in the fields of Melanoma detection overlaps with other disciplines such as Lesion segmentation, Disease classification, Medical physics and Receiver operating characteristic.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A comparative study of efficient initialization methods for the k-means clustering algorithm

M. Emre Celebi;Hassan A. Kingravi;Patricio A. Vela.
Expert Systems With Applications (2013)

944 Citations

Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Noel C. F. Codella;David Gutman;M. Emre Celebi;Brian Helba.
international symposium on biomedical imaging (2018)

915 Citations

A Methodological Approach to the Classification of Dermoscopy Images

M. Emre Celebi;Hassan A. Kingravi;Bakhtiyar Uddin;Hitoshi Iyatomi.
Computerized Medical Imaging and Graphics (2007)

588 Citations

Lesion border detection in dermoscopy images.

M.Emre Celebi;Hitoshi Iyatomi;Gerald Schaefer;William V. Stoecker.
Computerized Medical Imaging and Graphics (2009)

443 Citations

An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm

Hitoshi Iyatomi;Hitoshi Iyatomi;Hiroshi Oka;M.Emre Celebi;Masahiro Hashimoto.
Computerized Medical Imaging and Graphics (2008)

263 Citations

Border detection in dermoscopy images using statistical region merging.

M. Emre Celebi;Hassan A. Kingravi;Hitoshi Iyatomi;Y. Alp Aslandogan.
Skin Research and Technology (2008)

256 Citations

Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC).

Noel C. F. Codella;Veronica Rotemberg;Philipp Tschandl;M. Emre Celebi.
arXiv: Computer Vision and Pattern Recognition (2019)

193 Citations

Unsupervised border detection in dermoscopy images

M. Emre Celebi;Y. Alp Aslandogan;William V. Stoecker;Hitoshi Iyatomi.
Skin Research and Technology (2007)

191 Citations

Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images

Michael A. Marchetti;Noel C.F. Codella;Stephen W. Dusza;David A. Gutman.
Journal of The American Academy of Dermatology (2018)

190 Citations

Improving the performance of k-means for color quantization

M. Emre Celebi.
Image and Vision Computing (2011)

173 Citations

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