A Survey On Context Based Medical Image Processing Using Machine Learning

Abstract

Capturing context for supporting a particular scenario depends on various constructs like data history,
imperfections, timeliness, dependencies and relationships among contextual facts. Context refers to any piece of information that
can characterize any situation of an entity. The entity refers to any person, place or object. This property of characterizing any
entity plays a significant role in context- aware applications, such as image retrieval based on contextual inputs, video
surveillance, biomedical applications such as repairing lost cognition, creation of ensemble classifier. A Context based image
processing is the process of a image which is situation it accruing and it case to show the conditional position in various images
like historical images, face detection image, hands on movement image, medical MRI image, Data analytical images, videos
signal processing, and binary content and context image etc. this all the case which is shows to the situation related motion of
scene image. Machine learning algorithm support for the implementation of segmentation and classification of data knowledge.
In this paper we concentrate on a diabetic patient suffer with comorbidities. A diabetes person can cause with Covid-19(lungs
MRI-images Scan) and nephrology diagnosis classification.