Reviewing Machine Learning and Image Processing Based Decision-Making Systems for Breast Cancer Imaging


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Publication Details

Output type: Journal article

UM6P affiliated Publication?: Yes

Author list: Zerouaoui H., Idri A.

Publisher: Springer Verlag (Germany)

Publication year: 2021

Journal: Journal of Medical Systems (0148-5598)

Volume number: 45

Issue number: 1

Start page: 8

ISSN: 0148-5598

eISSN: 1573-689X

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100125842&doi=10.1007%2fs10916-020-01689-1&partnerID=40&md5=3074b3d443373f75f5a3c1c6134512ee

Languages: English (EN-GB)


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Abstract

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.


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Last updated on 2021-16-05 at 23:18