Application of Cognitive Information Systems in Medical Image Semantic Analysis

Abstract

Cognitive information systems create a new class of intelligent systems focused on semantic data analysis tasks. Such systems are based on cognitive resonance processes, which use a knowledge-based perception model, to analyze and semantically classify visual data. Such systems can therefore be used for image analysis and classification, including semantic analysis of medical images, aimed at supporting diagnostic processes and determining the severity of lesions visualized by diagnostic imaging methods. This paper will describe various types of cognitive information systems designed for lesion recognition in selected abdominal and coronary structures, as well as skeletal parts of the human body, made visible by the application of various modalities in medical diagnostic imaging procedures. In this paper, a new generation of cognitive systems will also be described, and when compared to existing systems, will have the ability to perform extended cognitive resonance processes. Inference based on extended resonance inference allows the system to acquire additional knowledge, as well as expand the knowledge base used for semantic analysis. This paper will also propose the implementation of new efficient formal grammars, which increase the efficiency of lesion recognition in selected medical images to over 90%.