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Artificial Intelligence in Breast Cancer Diagnosis: A Review

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Advances in Artificial Intelligence-Empowered Decision Support Systems

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 39))

Abstract

The impact of human errors in imaging interpretation and the fact that decision support systems can improve the reliability and accuracy of radiology reporting have led to the more widespread use of these techniques. Develo** decision support systems that assist radiologists in accurate diagnoses and improving the medical decision-making process has always been a challenge for the data analysis industry. This paper presents an in-depth review of a large number of intelligent approaches related to the support of breast cancer diagnosis taken in recent years. Specifically, the present review includes 230 corresponding approaches presented in the last 30 years in scientific journals in the field of artificial intelligence, as well as in medical journals. The search for the scientific reports included and presented in this paper was carried out in the databases of well-known scientific publishing houses using related keywords and phrases. The review briefly presents the main findings of each paper and classifies them according to their medical topic of specific interest (diagnosis, breast lesion classification, detection, abnormality classification, estimation of cancer risk, etc.). A statistical analysis is also provided, regarding the popularity of the approaches both, from the medical and AI viewpoint.

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Abbreviations

Ac:

Classification Accuracy

ACFNN:

Associative Classifier Using Fuzzy Feed-Forward Backpropagation Neural Network

AIRS:

Artificial Immune Recognition System

AMMLP:

Artificial Metaplasticity Multilayer Perceptrons

ANN:

Artificial Neural Networks

BIRADS:

Breast Imaging-Reporting and Data System

BP-a:

Back Propagation (BP) Training Algorithm

BRB-NN:

Back-Propagation Networks

CART:

Classification and Regression Trees

CCL:

Connected Component Labeling

CHAID:

Chi-Square Automatic Interaction Detection

CNN:

Convolutional Neural Networks

DCNN:

Deep Convolutional Neural Network

DEA:

Data Envelopment Analysis

DE-a:

Differential Evolution

ELM:

Extreme Learning Machine

EP:

Evolutionary Programming

FCM:

Fuzzy-C-Means

FISH:

Fisher classifier

FLD:

Fisher’s Linear Discriminant

FLS:

Fuzzy Logic System

FS:

Fuzzy Systems

GB-a:

Gentleboost Classifier (GB) Algorithm

GDA:

Gadratic Discriminant Analysis

GRNN:

General Regression Neural Networks

IDT:

Inductive Decision Tree

IG:

Information Gain

k-CV:

K-Fold Cross Validation

Kernel PCA:

Kernel Principal Component Analysis

KNN:

K-Nearest Neighbors

LBN:

Linear Bayes Normal Classifier

LDA:

Linear Discriminant Analysis

LOO:

Leave-1-Out

LR:

Logistic Regression

LYNA:

LYmph Node Assistant

MDSS:

Medical Decision Support System

MLP:

Multilayer Perceptron

NAÏVE:

Naive Bayes Classifier

OBL-a:

Opposition-Based Learning Algorithm

PCA:

Principal Component-Analysis

PDE:

Pareto-Differential Evolution

PNN:

Probabilistic Neural Networks

PSO:

Particle Swarm Optimization

QD:

Quadratic Classifier

RBFN:

Radial Basis Function Network

RF:

Random Forest

ROI:

Regions of Interest

RVM:

Relevance Vector Machine

SFFS:

Sequential Forward Floating Selection

SFS:

Sequential Forward Selection

SMO:

Sequential Minimal Optimization

Sn:

Sensitivity

SOM:

Self-Organizing Map

Sp:

Specificity

SVM:

Support Vector Machines

UTS:

Use-Test-Set

WPSO:

Weighted-Particle Swarm Optimization

YOLO:

You Only Look Once

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Authors and Affiliations

  1. Management and Decision Engineering Laboratory (MDE-Lab), School of Engineering, University of the Aegean, 41 Kountouriotou Street, 82100, Chios, Greece

    Evangelos Karampotsis & Georgios Dounias

  2. First Department of Radiology (‘Aretaieio’ Hospital), Medical School, National and Kapodistrian University of Athens, 76 V.Sofias, 11523, Athens, Greece

    Evangelia Panourgias

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  1. Evangelos Karampotsis
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  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  3. Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Haris Doukas

  4. KES International, SELBY, UK

    Lakhmi C. Jain

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Karampotsis, E., Panourgias, E., Dounias, G. (2024). Artificial Intelligence in Breast Cancer Diagnosis: A Review. In: Tsihrintzis, G.A., Virvou, M., Doukas, H., Jain, L.C. (eds) Advances in Artificial Intelligence-Empowered Decision Support Systems. Learning and Analytics in Intelligent Systems, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-031-62316-5_2

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