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Journal of Medical Systems

, 42:226 | Cite as

Medical Image Analysis using Convolutional Neural Networks: A Review

  • Syed Muhammad Anwar
  • Muhammad Majid
  • Adnan Qayyum
  • Muhammad Awais
  • Majdi Alnowami
  • Muhammad Khurram Khan
Image & Signal Processing
  • 470 Downloads
Part of the following topical collections:
  1. Image & Signal Processing

Abstract

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Keywords

Convolutional neural network Computer aided diagnosis Segmentation Classification Medical image analysis 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Software EngineeringUniversity of Engineering and Technology TaxilaTaxilaPakistan
  2. 2.Department of Computer EngineeringUniversity of Engineering and Technology TaxilaTaxilaPakistan
  3. 3.Centre for Vision, Speech and Signal Processing (CVSSP)University of SurreyGuildfordUK
  4. 4.Department of Nuclear EngineeringKing Abdul Aziz UniversityJeddahSaudi Arabia
  5. 5.Center of Excellence in Information Assurance (CoEIA)King Saud UniversityRiyadhSaudi Arabia

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