# Special Issue on Signal Processing and Machine Learning for Biomedical Data
## 3 Methods Based on Traditional Machine Learning  
There are many techniques that the traditional machine learning approach makes available for the analysis of biomedical images. There are many families of classifiers and training methods that could be used. The ML techniques presented in this special issue were therefore varied.  
The authors of [9] in their paper on _Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features_ proposed a system that is able to segment and classify systolic and diastolic intervals of phonocardiogram (PCG) signals of heart sounds. The classification is binary and allows us to discriminate between normal and abnormal heart sounds. The authors performed feature extraction based on power values in the systolic and diastolic intervals and trained four classifiers--SVM, KNN, random forest, and MLP. The best accuracy result was 99.26%, using the KNN classifier.  
In [10], the authors proposed Net-Net AutoML models for the selection of ANNs for the study of Brain Connectome Networks (BCNs) prediction. To predict BCN node connectivity, the Net-Net AutoML evaluates other ANNs trained to predict BCN connectivity.  
The following twelve machine learning classifiers were tested--KNN, LDA, GBN, SVM, LogR, MLP, DT, RF, XGB, GB, AdaBoost, and Bagging. The performance was expressed in terms of AUC (Area Under the Curve ROC), by considering a 10-fold cross validation strategy and the best classification was achieved through the Random Forest classifier.  
[PERSON] et al. [11] addressed the identification of clustered microcalcifications in digital mammography. The public database used was the BCDR-DM; in particular 104 digital mammograms containing microcalcifications were used. SURF and MinEigenAlg algorithms were used to extract 96 regions of interest (ROIs), 56 benign and 40 malignant, from the mammograms. Each ROI underwent a multiscale decomposition process, based on the Haar wavelet transform and the gray-level co-occurrence matrix. The classification phase of microcalcification clusters into benign and malignant was carried out with the random forest classifier, and the performance obtained was an AUC of 94.19%.  
In [12], the authors addressed the detection of breast cancer with a novel classification system, based on biological immune systems processes. Their model called AISAC (Artificial Immune System for Associative Classification) was compared with ten classification systems--three immune-based classification algorithms (AIRS1, Immunos1, and CLON-ALG), and six general-purpose classifiers (Support Vector Machines, multilayer perceptron, nearest neighbor, RIPPER, C4.5, naive Bayes, random forest).  
The authors of [13] proposed the use of the multiscale filter responses of the Gaussian Matched Filter (GMF) and the Gabor filters, coupled with the multilayer perceptron network (MLP) for the automatic segmentation of coronary arteries in X-ray angiograms. The coronary artery detection was carried out with a four-layered perceptron network. For the final segmentation, each pixel was discriminated between two classes--vessel features and image background. The results were obtained from a public database with 130 X-ray coronary angiograms, and their corresponding ground-truth image was outlined by an expert cardiologist. The authors obtained an AUC for detection of 98%.  
[PERSON] et al. [14] addressed discrimination between younger/older normal sinus rhythm (NSR) and congestive heart failure (CHF), by analyzing the electrocardiogram (ECG) signal. Using the multiscale entropy (MSE) algorithm, they extracted 20 features and conducted a feature selection. The classification phase was addressed with the SVM, KNN, and LDA classifiers, using the leave-one-out cross-validation strategy.