Developing An Association Rule Based Method To Support Medical Image Diagnosis With Efficiency

Data mining in brain imaging is an emerging field of high importance for providing prognosis, treatment, and a deeper understanding of how the brain functions. The discovery of associations between human brain structures and functions (i.e. human brain mapping) has been recognized as the main goal of the Human Brain Project. The field of data mining addresses the question of how best to use the data to discover new knowledge and improve the process of decision making. The extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing and accessing of data, data analysis and effective use of stored knowledge of data. Association Rules Technique help in analyzing and retrieving hidden patterns for a large volume of data collected in a medical database for a large hospital. This paper proposes a method based on association rule-mining to enhance the diagnosis of medical images (brain). It combines low level features automatically extracted from images and high-level knowledge from specialists to search for patterns. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. The experimental result on prediagnosed database of brain images showed 97% sensitivity and 95% accuracy respectively. The physicians can make use of this accurate result in order to classify the brain images into normal, benign and malignant for effective medical diagnosis.