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Deep Learning (DL) is a novel type of Machine Learning (ML) model. It is showing increasing promise in
medicine, study and treatment of diseases and injuries, to assist in data classification, novel disease symptoms and
complicated decision making. Deep learning is the form of machine learning typically implemented via multi-level neural
networks. This work discuss the pros and cons of using DL in clinical cardiology that also apply in medicine in general,
while proposing certain directions as the more viable for clinical use. DL models called deep neural networks (DNNs),
recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been applied to arrhythmias,
electrocardiogram, ultrasonic analysis, genomes and endomyocardial biopsy. Convincingly, the rusults of trained model
are good, demonstrating the power of more expressive deep learning algorithms for clinical predictive modeling. In the
future, more novel deep learning methods are expected to make a difference in the field of clinical medicines.
Multi-Level Texture Encoding and Representation (Multer) Based on Deep Neural Networks
