A precise analysis of deep learning for medical image processing

Abstract

Recently, usage of image processing in machine learning (ML) is growing fast. Medical image processing, image segmentation, computer-aided diagnosis, image transformation, image fusion combined with AI play a crucial role in the healthcare field. Other industries are different from the healthcare sector. This is the people’s highest priority sector for those whose expectation levels of the people about care and services are high at a decent cost. It consumes a huge percentage of budgets, but still, it does not affect the social expectations. Many times it is observed that explanations provided by medical experts seem to be ambiguous. Few experts are able to effectively explain the details of medical images due to its complexity and subjective nature; interpreters and fatigue exist in different extensives. Later, the achievement of deep learning concept in varieties of real-time application domains also provides thrilling solutions with a good accuracy percentage in a medical image which will help the medical society soon. Deep learning (DL) methods are a set of algorithms in machine learning (ML), which provides an effective way to analyse medical images automatically for diagnosis/assessment of a disease. DL enables a higher level of abstraction and provides better prediction from data sets. Therefore, DL has a great impact and becomes popular in recent years. In this chapter, we discuss different states of deep learning architecture, image classifications and the medical image segmentation optimizer. This chapter provides a detailed analysis of algorithms based on deep learning that is used in clinical image with regards to recent works and their future approaches. It provides some important knowledge and the way of approaching deep learning concept in the field of healthcare image analysis. Afterwards, we will discuss the challenges that are faced when it is applied to medical images and some open research issues. In the end, a successful medical image processing is presented where implementation is done by deep learning.