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...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 11; pp. 1 - 2 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas review tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132813859&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132813859 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2018 vid: 42 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132813859 132813859 132813859 10.1007/s10916-018-1088-1 132813859 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Medical Image Analysis using Convolutional Neural Networks: A Review. aug: au: Anwar, Syed Muhammad Majid, Muhammad Qayyum, Adnan Awais, Muhammad Alnowami, Majdi Khan, Muhammad Khurram affil: Department of Software Engineering, University of Engineering and Technology Taxila, 47050, Taxila, Pakistan sug: subj: Diagnosis, Computer Assisted Diagnostic Imaging Evaluation Neural Networks (Computer) Image Interpretation, Computer Assisted Biomedical Engineering Machine Learning Methods Image Retrieval Image Processing, Computer Assisted Image Retrieval Systems Artificial Intelligence Imaging, Three-Dimensional ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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