Deep Learning Interpretation of Biomedical Data: Healthcare Transformation

Deep learning can be stated as a new field in the area of machine learning related with artificial intelligence. This learning technique resembles human functions in processing and defining patterns used for decision making. Deep learning algorithms are mainly developed using neural networks performing unsupervised data that are unstructured. These learning algorithms perform feature extraction and classification for identification of the system patterns. Deep learning also defined as deep neural network or deep neural layer possess different layers for processing of the learning algorithms that helps in active functioning and detection of patterns. Deep learning network consists of basic conceptual features like layer and activation function. Layer is the highest building block of deep learning process which can be categorised based on its function. Deep learning used in various applications, one among them is the field of Biomedical Engineering where big data observations are made in form of bio signals, medical images, pathological reports, patient history and medical reports. Biomedical data possess time and frequency domain features for analysis and classification. The study of large amount of data can be performed using deep learning algorithms. Thus, deep learning algorithms are used for interpretation and classification of biomedical big data.