Study Methods Input Purpose Dataset Classifier Results Dheeba et al. [3 ] Particle Swarm Optimized Wavelet Neural Network (PSOWNN) Mammogram Improve classification accuracy in breast cancer detection and reducing misclassification rate 216 mammograms PSOWNN (i) Sensitivity 94.167% Xu et al. [10 ] New algorithm based on two ANNs Mammogram Classification of masses 30 cases and 60 mammograms ANFIS and MLP (i) True positive (TP) rate 93.6% (73/78), Alayliogh and Aghdasi [11 ] ANN and biorthogonal spline wavelet Mammogram Classification of microcalcification cluster (MCC) and image enhancement 40 digitized mammogram ANN (i) Sensitivity 93%, Dhawan et al. [13 ] (i) ANN Mammogram Classification of significant and benign microcalcifications 5 image structure features (i) Three-layer perceptron based ANN The entropy feature has significant discriminating power for classification Chitre et al. [15 ] ANN Mammogram Classification of microcalcification into benign and malignant (i) 40, 60, and 80 training cases ANN Neural network is a robust classifier of a combination of image structure and binary features into benign and malignant Kevin et al. [16 ] ANN Mammogram Classification of microcalcifications and nonmicrocalcifications 24 mammograms with Cascade correlation ANN (i) TP detection rate for individual Zheng et al. [18 ] ANN and BBN Mammogram Compare performances of ANN and BBN 3 independent image databases and 38 features ANN and BBN Performance level (A z Zhang et al. [19 ] Digitize module, detection module, feature extraction module, neural network module, and classification module Mammogram Classification of microcalcification clusters/suspicious areas Fuzzy detection algorithm Backpropagation neural network (BPNN) (i) Fuzzy detection rate (benign 84.10% and 80.30%) Lashkari [20 ] ANN and Gabor wavelets Mammogram Classification of breast tissues to normal and abnormal classes automatically (i) Images of 50 normal and 50 ANN and Gabor wavelets (i) Classification rate (testing performance 96.3% and training performance 97.5%) Saini and Vijay [17 ] Image registration technique and ANN Mammogram Classification of benign and malignant 42 mammogram images (30 benign and 12 malignant images) Feed-forward backpropagation and Cascade forward Percentage accuracy Buller et al. [21 ] Spider web topology with NN Ultrasound Classify and separate benign and malignant lesion 25 sonograms (i) NN classifier (i) 69% accuracy in malignant Ruggierol et al. [22 ] Texture and shape parameter feeds into NN Ultrasound Automated recognition of malignant lesion (i) 41 carcinomas (i) NN classifier (i) 95% accuracy in solid lesions Sahiner et al. [23 ] Convolutional NN with spatial and texture image Mammogram Classification of mass and normal breast 168 mammograms (i) Convolution NN classifier (i) Average true positive fraction of 90% at false positive fraction of 31% Chen et al. [24 ] Multilayer feed-forward neural network (MFNN) Ultrasound Classify benign and malignant lesion 140 pathological proved tumors (52 malignant, 88 benign) MFNN (i) 95% accuracy, 98% sensitivity Chen et al. [25 ] Self-organizing map (SOM) Ultrasound Classification of benign and malignant lesions 243 tumors (82 malignant, 161 benign) SOM (i) Accuracy of 85.6, sensitivity 97.6% Chen et al. [27 ] Bootstrap with NN Ultrasound classification of tumor 263 sonographic image solid breast nodules NN (i) Accuracy 87.07%, sensitivity 98.35% Chen et al. [26 ] 2-phase Hierarchical Neural Network (HNN) Ultrasound Differentiate between benign and malignant tumors 1020 images (4 different rectangular regions from the 2 orthogonal planes of each tumor) HNN 4 image analyses of each tumor appear to give more promising result than if they are used separately Chen et al. [28 ] Wavelet transform and neural network Ultrasound Differential diagnosis of breast tumors on sonograms 242 cases (161 benign, 82 malignant) Multilayer perceptron neural network (MLPNN) (i) Receiver operating characteristic (ROC) area index is 0.9396 ± 0.0183 Chen et al. [29 ] Multilayer feed-forward neural network (MFNN) Ultrasound Differentiate benign from malignant breast lesions 1st set: 160 lesions MFNN (i) 98.2% training accuracy Joo et al. [30 ] Artificial neural network (ANN) Ultrasound Determining whether a breast nodule is benign or malignant 584 histologically confirmed cases (300 benign, 284 malignant) ANN (i) 100% training accuracy Joo et al. [31 ] Digital image processing and artificial neural network Ultrasound Determine breast nodule malignancy 584 histologically confirmed cases (300 benign, 284 malignant) ANN (i) 91.4% accuracy, 92.3% sensitivity Zheng et al. [32 ] Hybrid method (unsupervised k -means cluster, supervised backpropagation neural network (BPNN)) Ultrasound Classification of breast tumors as benign or malignant 125 benign tumors, 110 malignant tumors Combination of k -means with BPNN (i) Recognition rate (94.5% for benign, 93.6% for malignant) Fok et al. [35 ] ANN with 3D finite element analysis IR Tumor prediction 200 patients ANN Good detection, poor sensitivity Szu et al. [36 ] Unsupervised classification using Lagrange Constraint Neural Network (LCNN) Mid and long IR images Early detection of breast cancer One patient with DCIS LCNN Better sensitivity Jakubowska et al. [37 ] ANN with wavelet transform IR Discrimination of healthy and pathological cases 30 healthy ANN Accuracy (%) 10 with recognized tumors Accuracy (%) Koay et al. [33 ] Backpropagation NN IR Early detection of breast cancer 19 patients Levenberg-Marquardt (LM) and Resilient Backpropagation (RP) Accuracy (%) Tan et al. [34 ] Fuzzy adaptive learning control network fuzzy neural network IR Early detection of breast cancer and tumor classification 28 healthy, 43 benign tumors, 7 cancer patients FALCON-AART Cancer detection (%) (TH/TDF) Cardillo et al. [40 ] NN for automatic analysis of image statistics MRI Early detection and classification 150 exams subdivided into 6 groups by contrast NN Better in specificity Tzacheva et al. [41 ] Evaluation of signal intensity and mass properties by NN MRI Automatic diagnosis of tumors 14 patients Feed-forward BPNN 90%–100% sensitivity, 91%–100% specificity, and 91%–100% accuracy Ertas et al. [39 ] Extraction of breast regions by conventional and multistate CNNs MRI Breast density evaluation and abnormality localization 23 women CNN Average precision 99.3 ± 1.8% Hassanien et al. [38 ] Image classification using PCNN and SVM and using wavelet and fuzzy sets for enhancement MRI Breast cancer detection 70 normal cases, 50 benign and malign cases Hybrid scheme of PCNN and SVM Accuracy ElNawasany et al. [42 ] Classifying MR images by hybrid perceptron NN MRI Early detection of breast cancer 138 abnormal and 143 normal Perceptron with SIFT Accuracy 86.74%
