Major Techniques of Image Processing with Deep Learning Neural Network and its Applications in Health Sector: A Critical Study

Abstract

Health care organizations in this highly developing world are leveraging different machine-learning techniques, such as artificial neural networks (ANN), Artificial Intelligence (AI), and so on to improve the quality of care at a reduced cost. The applications of ANN in the field of medical imaging used for various diagnosis are well-known; however, an emerging field of Deep Learning which is a form of Machine learning is replacing the traditional techniques. The Deep learning provides a modern cost-effective method of training Deep Neural Networks (DNNs) which have previously been hesitant to understand weights. Additional layers in the DNN allow for the composition of attributes from lower layer to upper layer, allowing complex information to be modelled. Today, some machine vision systems focused on deep learning are performing much better than humans, i.e. recognizing cancer markers in MRI scans and so on. The purpose of this article is to discuss the application of deep learning in the field of Medical imaging and how it is gives an edge over the traditional Machine learning methods.