Optimization techniques for certain classification and clustering problems in medical images

Abstract

Medical diagnosis is considered as a significant yet intricate task that needs to be carried out precisely and effectively Clinical decisions are often made based on intuition and experience of Medical experts rather than on the knowledge of rich data hidden in the database Medical Experts must go through continuous training to make good clinical decisions They rely on the background knowledge current research and professional experience This practice sometimes leads to unwanted bias errors and excessive medical newlinecosts which affect the quality of service provided to patients Since these decisions affect the welfare of patients the investigation of novel methods that can enhance the quality of medical decision making is of high importance Data Mining Algorithms have the potential to generate a knowledge rich environment which can help to significantly improve the quality of clinical decisions Magnetic Resonance Imaging is one of the effective and powerful medical imaging techniques used for diagnosis The main challenge in diagnosis of diseases is accuracy in Prediction In this work Optimized clustering and Classification algorithms are proposed to enhance accuracy of medical decision making process and to reduce False Positives in the diagnosis Process of Diseases from MR Images In order to support Medical Experts in diagnosis and treatment newlinefour optimized diagnostic approaches have been proposed in this research work with the aim of reducing or nullifying false positives and improving detection accuracy newline newline