World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
41
Citations
10252
World Ranking
6813
National Ranking
1853

Research.com Recognitions

  • 2019 - SPIE Fellow

Overview

Metin N. Gurcan is affiliated with Wake Forest University in the United States and has contributed extensively to research in the field of Medicine, with a total of 143 publications. Their work spans several subfields, including Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Molecular Biology, Epidemiology, and Surgery.

The scientist has authored research addressing a range of topics such as AI in cancer detection, Radiomics and Machine Learning in Medical Imaging, Cell Image Analysis Techniques, Ear Surgery and Otitis Media, Reconstructive Facial Surgery Techniques, Tuberculosis Research and Epidemiology, and Digital Imaging for Blood Diseases.

Frequent co-authors collaborating with Metin N. Gurcan include Muhammad Khalid Khan Niazi, Mostafa Rezapour, Thomas E. Tavolara, Ziyu Su, and Aaron C. Moberly.

Metin N. Gurcan has published papers in various well-known venues. Some of the more frequent publication venues are bioRxiv (Cold Spring Harbor Laboratory), arXiv (Cornell University), Alzheimer's & Dementia, Cancers, and PLoS ONE.

Representative recent papers include:

  • Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results, 2021, Cancers
  • Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring, 2022, Modern Pathology
  • Cloud-Based Federated Learning Implementation Across Medical Centers, 2021, JCO Clinical Cancer Informatics
  • Bioreactor design and validation for manufacturing strategies in tissue engineering, 2021, Bio-Design and Manufacturing
  • Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models, 2021, Computers in Biology and Medicine

In addition to articles, Metin N. Gurcan has published a book titled Brain Tumor Segmentation, Grading and Patient Survival Prediction in 2023 through Frontiers Media.

The scientist was recognized as an SPIE Fellow in 2019.

Best Publications

  • Histopathological Image Analysis: A Review

    M.N. Gurcan;L.E. Boucheron;A. Can;A. Madabhushi

  • Digital pathology and artificial intelligence

    Muhammad Khalid Khan Niazi;Anil V Parwani;Metin N Gurcan

  • Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study.

    Xiangchun Li;Sheng Zhang;Qiang Zhang;Xi Wei

  • Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system

    Metin N. Gurcan;Berkman Sahiner;Nicholas Petrick;Heang Ping Chan

  • Computer-aided prognosis of neuroblastoma on whole-slide images: Classification of stromal development

    O. Sertel;J. Kong;H. Shimada;U. V. Catalyurek

  • Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting

    Hui Kong;M. Gurcan;K. Belkacem-Boussaid

  • Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization

    B. Sahiner;N. Petrick;Heang-Ping Chan;L.M. Hadjiiski

  • Computerized classification of intraductal breast lesions using histopathological images

    M M Dundar;S Badve;G Bilgin;V Raykar

  • Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading

    Olcay Sertel;Jun Kong;Umit V. Catalyurek;Gerard Lozanski

  • Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation

    J. Kong;O. Sertel;H. Shimada;K. L. Boyer

  • Content-Based Microscopic Image Retrieval System for Multi-Image Queries

    H. C. Akakin;M. N. Gurcan

  • Image analysis for neuroblastoma classification: segmentation of cell nuclei.

    Metin N. Gurcan;Tony Pan;Hiroyuki Shimada;Joel H. Saltz

  • Coordinating the use of GPU and CPU for improving performance of compute intensive applications

    G. Teodoro;R. Sachetto;O. Sertel;M.N. Gurcan

  • Detection of microcalcifications in mammograms using higher order statistics

    M.N. Gurcan;Y. Yardimci;A.E. Cetin;R. Ansari

  • Computer-Aided Detection of Centroblasts for Follicular Lymphoma Grading Using Adaptive Likelihood-Based Cell Segmentation

    O Sertel;G Lozanski;M N Gurcan

  • Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results.

    Seda Camalan;Hanya Mahmood;Hamidullah Binol;Anna Luiza Damaceno Araújo

  • Analysis of temporal changes of mammographic features: computer-aided classification of malignant and benign breast masses.

    Lubomir Hadjiiski;Berkman Sahiner;Heang-Ping Chan;Nicholas Petrick

  • Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees.

    M.S. Swanson;J.W. Prescott;T.M. Best;K. Powell

  • Feature-based registration of histopathology images with different stains: An application for computerized follicular lymphoma prognosis

    Lee Cooper;Olcay Sertel;Jun Kong;Gerard Lozanski

  • Adaptive Discriminant Wavelet Packet Transform and Local Binary Patterns for Meningioma Subtype Classification

    Hammad Qureshi;Olcay Sertel;Nasir Rajpoot;Roland Wilson

  • Optimal neural network architecture selection: improvement in computerized detection of microcalcifications.

    Metin N. Gurcan;Heang Ping Chan;Berkman Sahiner;Lubomir Hadjiiski

  • Automatic graph-cut based segmentation of bones from knee magnetic resonance images for osteoarthritis research

    Sufyan Y. Ababneh;Jeffrey W. Prescott;Metin N. Gurcan

  • Texture classification using nonlinear color quantization: Application to histopathological image analysis

    O. Sertel;Jun Kong;G. Lozanski;A. Shana'ah

  • Vessel segmentation from abdominal magnetic resonance images: adaptive and reconstructive approach.

    Evgin Goceri;Zarine K. Shah;Metin N. Gurcan

  • Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images

    Caglar Senaras;Muhammad Khalid Khan Niazi;Berkman Sahiner;Michael P. Pennell

  • Computerized microscopic image analysis of follicular lymphoma

    Olcay Sertel;Jun Kong;Gerard Lozanski;Ümit V. Çatalyürek

Frequent Co-Authors

Joel H. Saltz
Joel H. Saltz Stony Brook University
Ümit V. Çatalyürek
Ümit V. Çatalyürek Georgia Institute of Technology
Berkman Sahiner
Berkman Sahiner United States Food and Drug Administration
Heang Ping Chan
Heang Ping Chan University of Michigan–Ann Arbor
Lubomir M. Hadjiiski
Lubomir M. Hadjiiski University of Michigan–Ann Arbor
Anant Madabhushi
Anant Madabhushi Emory University
Nicholas Petrick
Nicholas Petrick US Food and Drug Administration
Hiroyuki Shimada
Hiroyuki Shimada Stanford University
Rebecca D. Jackson
Rebecca D. Jackson The Ohio State University
Thomas M. Best
Thomas M. Best University of Miami

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