Deep Learning: A tool in Biomedical Science

Abstract

The development of technologies in health care and biomedical sciences outcomes with a large amount of data that limits the human capability, which highlights the urgency of predictive and analysis tools. Their outcomes will catalyst betterment judgments and decision-making in medical/healthcare organizations, at the same time; it'll also unfasten various prospects of research. In consideration of this, artificial intelligence and its branches not only replicate human intelligence but also analyze the data and manifest excellent results. Machine learning is one of them, that is assigned with computer learning patterns for a specific dataset and produces results that are new and have unseen data. This involves the least amount of human intervention as machines learn how to optimize themselves to produce astounding outcomes. But it also has its drawbacks as these algorithms do not learn accurately and lack the capability of learning deep patterns. Deep learning algorithms have been developed to overcome those drawbacks. In a deep learning environment, there are many layers or levels of abstraction which helps in defining the complexity of the patterns behind the data. The purpose of this paper is to explore the role of deep learning in healthcare and biomedical sector. Following that, evolution in artificial neural network (ANNs) and deep learning architecture is discussed.