# Special Issue on Signal Processing and Machine Learning for Biomedical Data
## 4 Methods Based on Deep Learning  
Over the past decade, the popularity of methods that exploited deep learning techniques increased considerably, evidently as deep learning improved the state-of-the-art methods in research fields, such as speech recognition and computer vision. In the field of computer vision, deep learning expressed its potential in image processing, also thanks to the Convolutional Neural Networks (CNNs). The authors of [15] in their paper on _Performance of Fine-Tuning Convolutional Neural Networks for HEP-2 Image Classification_ tackled the analysis of HEP-2 images for the diagnosis of autoimmune diseases. In particular, the classification of the fluorescence intensity was addressed. As recognized in the literature, this interpretation was particularly subjective, for this reason the authors chose to classify these images by means of the recent CNN networks. Four of the best known pre-trained CNN networks were used, namely; AlexNet, SqueezeNet, ResNet18, and GoogleNet. The authors analyzed both the technique that exploited the layers of pretrained networks as feature extractors to train a linear SVM, and the fine-tuning technique to conduct retraining. The retraining of all layers, called from scratch, was also analyzed and the rotation-based augmentation technique was compared. The performances obtained demonstrated the great classifying power of CNN reaching AUC values higher than 98%. [PERSON] et al. [16] developed a computer-aided design (CAD), capable of analyzing breast MRI images and classified breast cancer. The system consisted of two main processing levels--the segmentation of possibly tumoral ROIs and characterization of the selected ROIs between the in situ and invasive tumor. To select suspicious regionsthat were likely to contain a tumor mass, the authors used a deep learning method and in particular they used the pre-trained GoogleNet as an extractor of features from each ROI to train an ANN network. The tumor characterization consisted of Radiomics feature extraction (1820 features), features reduction phase, and classification with three different classifiers--Naive Bayes, random forest, and XGBoost. The performances obtained showed a sensitivity of 75% in mass detection and 70% of AUC for the binary classification. In [17], the authors developed a machine learning algorithm for the multi-class detection of three common types of voice disorders. Two publicly available databases for voice disorders were used. The problem of unbalanced data was addressed with the use of CGAN (conditional generative adversarial network) to increase the number of training data for classes with fewer samples, using synthetic data. The features extraction phase used four voice-quality-based parameters--harmonic to the noise ratio (HNR), shimmer, jitter, and fundamental frequency. The classification method used an improved fuzzy c-means clustering (IFCM) algorithm that considered the relationship between adjacent data points in the fuzzy membership function. The authors of [18] proposed an algorithm based on deep learning to improve the recognition rate of the left and right hand MI-EEG (Motor Imagery ElectroEncephaloGram) signals. To this end they used the public database \"BCI Competition IV Dataset 2b\" composed of nine subjects who participated in five sessions. The method was based on the following chain--filtering with a 4-35 Hz filter; mapping into a time-frequency image by applying CWT (continuous wavelet transform), use of a simplified convolutional neural network (SCNN), with the image as input and the binary classification as output. The performances obtained exceed traditional methods and were 83.2% of the average classification accuracy. [PERSON] et al. [19] addressed the problem of detection of the pneumonia diseases by analyzing chest X-ray images. The method was based on the use of 5 pre-trained CNNs (AlexNet, DenseNet121, InceptionV3, ResNet18, and GoogleNet) subjected to fine-tuning. The final image classification was carried out by the majority result obtained by the 5 CNNs.