Table 1 Metastatic and cancer prediction techniques.

Ahmad et al.³³

To obtain the highest accuracy

CNN-LSTM, CNN-GRU and AlexNet GRU are used. Out of these three AlexNet GRU outperforms

Kaggle PCam imaging dataset

99.5%

Choudhury³⁴

To diagnose and predict the cancer prognosis of Malignant Pleural Mesothelioma as early as possible (MPM)

8 different algorithms are used

Clinical data collected by Dicle University

79.29%

Bejnordi et al.³⁵

To investigate the predictive power of deep learning algorithms Vs 11 members of pathologists in a simulated time-constraint environment

In a research challenge competition. 32 deep learning models have been submitted by the contestants out of which 7 models showed a greater performance

Detecting lymph node metastases: A CAMELYON16 dataset

Area Under the Curve (AUC) of 0.994

Abdollahiet al.³⁶

To detect metastatic breast cancer using the whole-slide pathology images

Ensemble model consisting of VGG16, Resnet50, Google net, and Mobile net

CAMELYON16 dataset

98.84%

Papandrianos et al.³⁷

To identify bone metastasis of prostate cancer

Convolutional Neural Network (CNN)

Nuclear Medicine Department of Diagnostic Medical Center, Larisa, Greece

97.38%

Gupta, and Gupta³⁸

Deep learning approaches for predicting breast cancer survivability

Restricted Boltzmann Machine

The Surveillance, Epidemiology, and End Results (SEER) database

97%

Sharma and Mishra³⁹

Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis

voting classifier

Wisconsin Breast Cancer (WDBC)

99.41%

Ak⁴⁰

A comparative analysis of breast cancer detection and diagnosis using data visualization and machine learning applications

logistic regression model

Dr. William H. Walberg of the University of Wisconsin Hospital

98.1%

Maqsood et al.⁴¹

A breast cancer detection and classification towards computer-aided diagnosis using digital mammography in early stages

Transferable texture convolutional neural network (TTCNN)

DDSM, INbreast, and MIAS datasets

97.49%

Nanglia et al.⁴²

An enhanced predictive heterogeneous ensemble model for breast cancer prediction

Heterogeneous Stacking Ensemble Model

Coimbra breast cancer dataset

78%

Feroz et al.⁴³

Machine learning techniques for improved breast cancer detection and prognosis—a comparative analysis

K-Nearest Neighbor and Random Forest

Wisconsin

97.14%

Nasser⁴⁴

Application of Machine Learning Models to the Detection of Breast Cancer

Random forest

Breast Cancer Database of Coimbra

83.3%

Seo et al.⁴⁵

Scaling multi-instance support vector machine to breast cancer detection on the BreaKHis dataset

SVM

BreaKHis dataset

Alfian et al.⁴⁶

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

SVM

Gynaecology Department of the University Hospital Centre of Coimbra (CHUC)

80.23%

Afolayan et al.⁴⁷

Breast cancer detection using particle swarm optimization and decision tree machine learning technique

Particle swarm optimization and decision tree

Wisconsin breast cancer dataset

92.26%

Lakshmi, et al.⁴⁸

Breast cancer detection using UCI machine learning repository dataset Wisconsin Diagnostic Breast Cancer (WDBC) is the cell nuclei features extracted from medical imaging

The paper discusses 11 different machine-learning algorithms for classification. The classification pipeline used is as follows: (1) Min–Max normalization, (2) dimensionality reduction PCA and t-SNE, and (3) the Randon Forest classification method

Wisconsin Diagnostic Breast Cancer (WDBC)

99% accuracy