Medical Image Analysis using Convolutional Neural Networks: A Review

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...

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Veröffentlicht in:Journal of medical systems Jg. 42; H. 11; S. 226 - 13
Hauptverfasser: Anwar, Syed Muhammad, Majid, Muhammad, Qayyum, Adnan, Awais, Muhammad, Alnowami, Majdi, Khan, Muhammad Khurram
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2018
Springer Nature B.V
Schlagworte:
Algorithms
Artificial intelligence
Artificial neural networks
Biomedical engineering
Deep Learning
Diagnosis
Diagnosis, Computer-Assisted - methods
Health Informatics
Health Sciences
Image & Signal Processing
Image analysis
Image detection
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Information processing
Information Storage and Retrieval
Learning algorithms
Machine learning
Medical diagnosis
Medical imaging
Medical research
Medicine
Medicine & Public Health
Neural networks
Neural Networks (Computer)
R&D
Research & development
State-of-the-art reviews
Statistics for Life Sciences
Computer aided diagnosis
Segmentation
Convolutional neural network
Classification
Medical image analysis
ISSN:0148-5598, 1573-689X, 1573-689X
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-018-1088-1