Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging.
Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper c...
| Publicado en: | Journal of Medical Systems Vol. 45; no. 1; pp. 1 - 21 |
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| Autores principales: | , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
2021
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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=147997209&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147997209 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2021 vid: 45 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 147997209 147997209 147997209 10.1007/s10916-020-01689-1 147997209 ppf: 1 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging. aug: au: Zerouaoui, Hasnae Idri, Ali affil: Modeling, Simulation and Data Analysis, Mohammed VI Polytechnic University, Benguerir, Morocco sug: subj: Machine Learning Methods Image Processing, Computer Assisted Methods Decision Making, Computer Assisted Methods Breast Neoplasms Diagnosis Diagnostic Imaging Methods Human Systematic Review Descriptive Statistics Deep Learning Breast Neoplasms Classification Magnetic Resonance Imaging Breast Neoplasms Ultrasonography PubMed Checklists Selection Bias Mammography Breast Neoplasms Pathology Support Vector Machine Neural Networks (Computer) Funding Source Diagnosis, Computer Assisted Sensitivity and Specificity Decision Trees Algorithms ab: Breast cancer (BC) is the leading cause of death among women worldwide. It affects in general women older than 40 years old. Medical images analysis is one of the most promising research areas since it provides facilities for diagnosis and decision-making of several diseases such as BC. This paper conducts a Structured Literature Review (SLR) of the use of Machine Learning (ML) and Image Processing (IP) techniques to deal with BC imaging. A set of 530 papers published between 2000 and August 2019 were selected and analyzed according to ten criteria: year and publication channel, empirical type, research type, medical task, machine learning techniques, datasets used, validation methods, performance measures and image processing techniques which include image pre-processing, segmentation, feature extraction and feature selection. Results showed that diagnosis was the most used medical task and that Deep Learning techniques (DL) were largely used to perform classification. Furthermore, we found out that classification was the most ML objective investigated followed by prediction and clustering. Most of the selected studies used Mammograms as imaging modalities rather than Ultrasound or Magnetic Resonance Imaging with the use of public or private datasets with MIAS as the most frequently investigated public dataset. As for image processing techniques, the majority of the selected studies pre-process their input images by reducing the noise and normalizing the colors, and some of them use segmentation to extract the region of interest with the thresholding method. For feature extraction, we note that researchers extracted the relevant features using classical feature extraction techniques (e.g. Texture features, Shape features, etc.) or DL techniques (e. g. VGG16, VGG19, ResNet, etc.), and finally few papers used feature selection techniques in particular the filter methods. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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