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Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis

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Abstract

Cancer is a fatal disease caused due to the undesirable spread of cells. Breast carcinoma is the most invasive tumors and is the main reason for cancer deaths in females. Therefore, early diagnosis and prognosis have become necessary to increase survivability and reduce death rates in the long run. New artificial intelligence technologies are assisting radiologists in medical image scrutiny, thereby improving cancer patients’ status. This survey enrolls peer-reviewed, newly developed computer-aided diagnosis (CAD) systems implementing machine learning (ML) and deep learning (DL) techniques for diagnosing breast carcinoma, compares them with previously established methods, and provides technical details with the pros and cons for each model. We also discuss some open issues, research gaps, and future research directions for the advanced CAD models in medical image analysis. Over the past decade, machine learning and deep learning have emerged as a subfield of artificial intelligence (AI), whose healthcare industry applications have provided excellent results with reduced cost and improved efficiency. This survey analyzes different classifiers of machine learning and deep learning approaches for breast cancer diagnosis. Results from previous studies proved that deep learning outperforms conventional machine learning for diagnosing breast carcinoma when the dataset is broad. Research gaps from the recent studies depict that practical and scientific research is an urgent necessity for improving healthcare in the long run.

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Abbreviations

AI:

Artificial intelligence

ML :

Machine learning

DL :

Deep learning

CAD:

Computer-aided diagnosis

ANN:

Artificial neural network

CNN:

Convolutional neural network

DCNN:

Deep convolutional neural network

SFM:

Screen film mammography

FFDM :

Full-field digital mammography

DBT:

Digital breast tomosynthesis

MRI:

Magnetic resonance imaging

HP:

Histopathology

US:

Ultrasound

UCI :

University of California Irvine

WBCD:

Wisconsin Breast Cancer Dataset

MIAS:

Mammography Image Analysis Society

DDSM:

Digital Database for Screening Mammography

PCA:

Principal component analysis

SVM:

Support vector machine

KNN:

K-nearest neighbor

LR:

Logistic regression

RBF:

Radial basis function

NB:

Naïve Bayes

DT:

Decision tree

GPU :

Graphical processing unit

LASSO:

Least absolute shrinkage and a selection operator

ROI:

Region of interest

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Authors and Affiliations

  1. Department of Computer Science and Engg, Delhi Technological University, Delhi, India

    Gunjan Chugh & Shailender Kumar

  2. Department of Computer Science, Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India

    Nanhay Singh

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  1. Gunjan Chugh
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Correspondence to Shailender Kumar.

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Chugh, G., Kumar, S. & Singh, N. Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis. Cogn Comput 13, 1451–1470 (2021). https://doi.org/10.1007/s12559-020-09813-6

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