rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Medical imaging
    sub title: artificial intelligence, image recognition, and machine learning techniques
  name:
    name part: Santosh, K. C.
    role:
      role term: editor.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  genre: Internet Resources.
  origin info:
    place:
      place term: flu
    date issued: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI), image recognition (IR) and machine learning (ML) algorithms/techniques. Further, coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models, TB type detection using slice separation approach, brain tumor image segmentation via deep learning, mammogram mass separation, epileptic seizures, breast ultrasound images, knee joint x-ray images, bone fracture detection and labeling, and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging.
  table of contents: Preface -- Editors -- 1. A novel stacked model ensemble for improved TB detection in chest radiographs / -- 2. The role of artificial intelligence (AI) in medical imaging: general radiologic and urologic applications -- 3. Early detection of epileptic seizures based on scalp EEG signals -- 4. Fractal analysis in histology classification of non-small cell lung cancer -- 5. Multi-feature-based classification of osteoarthritis in knee joint X-ray images -- 6. Detection and classification of non-proliferative diabetic retinopathy lesions -- 7. Segmentation and analysis of CT images for bone fracture detection and labeling -- 8. A systematic review of 3D imaging in biomedical applications -- 9. Review on the evolution of comprehensive information for digital sliding of pathology and medical image segmentation -- 10. Pathological medical image segmentation: a quick review based on parametric techniques -- Index.
  note: [edited by] KC Santosh, Sameer Antani, DS Guru, Nilanjan Dey.
  note: Includes bibliographical references and index.
  note: Dr. K.C. Santosh is an Assistant Professor and Graduate Program Coordinator (GPC) of the department of computer science at the University of South Dakota (USD). Also, Dr. Santosh serves School of Computing and IT, Taylor's University as an Adjunct Associate Professor. Before joining USD, Dr. Santosh worked as a research fellow at the U.S. National Library of Medicine (NLM), National Institutes of Health (NIH). He worked as a postdoctoral research scientist at the LORIA research centre, Universite de Lorraine in direct collaboration with industrial partner ITESOFT, France. He also worked as a research scientist at the INRIA Nancy Grand Est research centre, France, where, in just 3 years, he has received his PhD diploma in computer science. Before that, he worked as a graduate research scholar at the SIIT, Thammasat University, Thailand. Dr. Santosh demonstrated expertise in artificial intelligence, machine learning, computer vision, pattern recognition and image processing with various applications in medical image analysis, graphics recognition, document information content exploitation, biometrics and forensics. He published more than 120 peer-reviewed research articles; two authored books (Springer) and edited several books (Springer, Elsevier and CRC press), journal issues (Springer) and conference proceedings (Springer). Dr. Santosh serves as an associate editor of the International Journal of Machine Learning & Cybernetics (Springer). For more information, visit: kc-santosh.org. Sameer Antani, PhD is a versatile researcher leading several scientific and technical research projects. He applies his expertise in biomedical image informatics, automatic medical image interpretation, machine learning, information retrieval, computer vision, and related topics in computer science and engineering technology toward advancing the role of computational sciences in biomedical research, education, and clinical care. His current R & D projects include: anautomatic screening …
  subject:
    topic: Diagnostic imaging
  subject:
    topic: Diagnostic Imaging
  subject:
    topic: Imagerie pour le diagnostic
  subject:
    topic: COMPUTERS
    topic: Machine Theory
  subject:
    topic: TECHNOLOGY
    topic: Electricity
  subject:
    topic: TECHNOLOGY
    topic: Imaging Systems
  subject:
    topic: Diagnostic imaging
  classification: RC78.7.D53 M43215 2020eb
  classification: 616.07/54
  classification: WN 180
  related item:
    title info:
      title: Medical imaging
    origin info:
      publisher: Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T & F Informa, plc, 2020
    identifier: (DLC) 2019015642
    identifier: (OCoLC)1097366108
  identifier: 9780429029417
  identifier: 0429029411
  identifier: 9780429642494
  identifier: 0429642490
  identifier: 9780429636158
  identifier: 0429636156
  identifier: 9780429639326
  identifier: 0429639325
  identifier: 9780367139612
  identifier: 0367139618
  identifier: 9780429029417 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: TYFRS
    record creation date: 190821
    record change date: 20241108165529.8
    record identifier: in00005519714
    language of cataloging:
      language term: eng