Table 5.

Machine learning methods.

Title Author Publication	Application & Dataset		Techniques		Parameters		Strengths		Limitations
Title: “Machine Learning and radiology” [1] Author: “Wang S., Summers R.M” Publication: “Elsevier 2012”	CAD for breast US, Brain MRI, Content based retrieval CT or MRI, Text Analysis Dataset: Varies Application wise For eg: 12,000 images for content based image retrieval CT or MRI images		SVM, Naive Bayes, Neural Networks, Linear Models, Graphs Matching, Cluster Analysis, PCA, kNN.		Costs, Accuracy, Disseminating Expertise		“Reduce cost, Improve Accuracy, Disseminating in short supply”		Machine learning statistical approaches are not defined.
Title: “A Comparative Study of Classification Algorithms in E-Health Environment” [2] Author: “ M.A. Hassan” Publication: “IEEE Conf (2016)”	Medical Images (E-Health Envirnment) Dataset: 600 instances from public hospital Saudi Arabia		Classification Algorithms (Bayes Net, Logistic, K Star, Stacking, JRIP, One R,PART, J48, LMT, RF)		Precision, TP “True Positive”, Recall, FP “False Positive”, F-Measure, Time, ROC Area		“ROC Area concludes Random Forest has highest Rate”. “Bayes Net, K star, Stacking, OneR, J48 take least time 0.01 followed by PART 0.08 sec, then Logistic with 5.4 Sec and LMT took 12.2 sec.” “Bayes Net is the best classifier for patient data set in terms of performance metrices with TP 0.987, FP 0.002, Precision Rate 0.988, Recall rate 0.987, F-measure 0.988,ROC 0.994,time 0.01 sec.”		Decision making of classifiers is limited on huge dataset
Title: “Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods” [13] Author: Shan, J., Alam, S.K., Garra, B., Zhang, Y. and Ahmed, T. Publication: Science Direct (2015)	CAD for breast Ulrasound Dataset: 283 US iMages (133 benign and 150 Malignant)		ANN,SVM, Decision Tree, Random Forest, Student’s t –test		Shape, Orientation, Margin, Echo Pattern, Posterior Feature		“Best ROC performance” “Better performance of clustered classifiers in a tumor classification task.”		Hybridization of classifiers has been ignored
Title: “Machine Learning Approaches in Medical Image Analysis: From detection to diagnosis” [18] Author: “Bruijne M.” Publication: “Elsevier (2016)”	Detection of Diabetic Retinopathy, Brain MRI Images etc Dataset: 35,000 images of Diabetic Retinopathy.		Machine Learning Diagnosis Methods, Imaging Protocols, Labels		Confounding Factors- Age, Gender, Curves Visual Performance		“Train strong Models on little data, Improve access on Data, Best make use of image structure, Properties in designing models”		Theoretical base is explained
Title Author Publication		Application & Dataset		Techniques		Parameters		Strengths		Limitations
Title: “Hybrid Approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning” [19] Author: “H. Ravishankar, S. Prabhu, V. Vaidya, N. Singhal” Publication: “IEEE Conf (2016)”		Ultrasound of Fetal Abdomen Dataset: 70 Images		“Convolutional Neural Networks” (CNN), ”Gradient Boosting Machine” (GBM)		“Gray Level Co-Occurrence Matrix” (GLCM), Haar, “ Local Binary Pattern”(LBP)”, “Histogram of Oriented Gradient” (HOG), “Support Vector Machine” (SVM), “Random Forest” (RF)		“HOG feature outperform Haar Features by more than 4%.” “DSC overlap over all 70 test cases of combined approach jumped to 0.9, which suggests a 5% and 6% improvement over GBMs and CNNs.” “Gestational Age (GA) difference was obtained for 78% for GBM and 75% for CNN.”		Parameters evaluation is not explained properly.
Title: “A Novel Approach for Classifying Medical Images using Data Mining Techniques” [20] Author: “Mangai J. A.” Publication: “IJCSEE (2013)”		Fundus Images Dataset: “32 very severe images and 61 normal Fundus images ”		“k nearest neighbor (kNN), Support Vector Machine(SVM)and Naïve Bayes(NB)”		Discretization Method:Receiver Operating Characteristics(ROC) in terms of accuracy and area Minimal Description Length (MDL)		AUC outperform “NB classification performance outstanding” “NB is 0.94 as compare to kNN and SVM”		Data set is limited to only fungus retinal images
Title: “Computer-aided diagnosis of breast masses using quantified BI-RADS findings” [21] Author: “Woo kyung moon” Publication:“Science Direct (2013)”		Breast CAD US images Dataset: 244 US images (166 Benign & 78 Malignant)		“Computer-aided analysis with quantitative information BI-RADS Method”, Chi-Square Test		Specificity, Accuracy, PPV, NPV, pAUC		“CAD quantitative combination (0.96 vs 0.93, p=0.18)” “Partial AUC (Area Under Curve) over 90% sensitivity of proposed as compared to Conventional CAD(0.90 vs 0.76, p<0.05)”		Use of all tumors in the feature selection process
Title: “Automated breast cancer detection and classification using ultrasound images: A survey” [22] Author: “Cheng H.D” Publication: ELSEVIER (2010)		Breast US Images Dataset: No Benchmark Database, vary DB1 to DB6 according to different papers.		Filters, Wavelet, Neural Network, Morphological Processing, Classifiers		“Specificity, Accuracy, Sensitivity, Positive predictive value (PPV), Negative predictive value (NPV), Matthew’s correlation coefficient(MCC)”		“Number of NPV and PPV are unbalanced then MCC gives better evaluation then Accuracy.” “More Breast CAD systems employs SVM, ANN and BNN method, MCC should be evaluated Criteria”		Performance Evaluation of the approaches is not described properly.