Healthcare Techniques Through Deep Learning: Issues, Challenges and Opportunities

Abstract

In artificial intelligence, deep learning (DL) is a process that replicates the working mechanism of the human brain in data processing, and it also creates patterns for decision making. Deep learning or neural networks have been deployed in several fields, such as computer vision, natural language processing, and speech recognition. It has been used in many healthcare applications for the diagnosis and treatment of many chronic diseases. These algorithms have the power to avoid outbreaks of illness, recognize and diagnose illnesses, minimize running expenses for hospital management and patients. This paper discusses the deep learning methods used in different healthcare fields, i.e., identifying depression, heart diseases, physiological signals, lymph node metastases from breast cancer, etc. These diseases are categorized into the central nervous system, cardiovascular system, and respiratory system. For each category, after summarizing the studies, comparison tables are laid down using some important factors. Different applications, tools, methods, and data sets used for DL models are leveraged. Finally, research opportunities and challenges being faced for deep learning models are discussed.

Keywords

• Deep learning
• health care
• nervous system
• respiratory system
• supervised and unsupervised learning