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Deep Learning algorithms have recently been reported to be successful in the analysis of images and voice. These algorithms, specifically Convolutional Neural Network (CNN), have also proven themselves to be highly promising on images produced by medical imaging technologies, as well. By use of deep learning algorithms, researchers have accomplished several tasks in this field including image classification, object and lesion detection and segmentation of different tissues in a medical image. Researchers mostly focused on medical images of neurons, retina, lungs, digital pathology, breast, heart, abdomen and skeleton system to take advantage of the Deep Learning approach. This study reviews literature studies of recent years that utilized Deep Learning algorithms on medical images in order to present a general picture of the relevant literature.

The task of performing speakerrecognition over voice recordings is an active research area in the relevantliterature in which many applications has been proposed so far. In this study, speaker recognition isperformed over cepstral features extracted from raw voice recordings. Some ofthe most prominent cepstral feature selection methods, namely, LPC, LPCC, MFCC, PLP andRASTA-PLP are utilized and their contribution to the performance of theapplied method is investigated. Obtainedfeatures are handled by SVM classification algorithm to finalize the speakerrecognition task. As a result, it is observedthat cepstral feature selection methods suchas LPCC and MFCC combined with SVM classification resultin around 97% accuracy.