A Framework for Medical Images Classification Using Soft Set

AbstractMedical images classification is a significant research area that receives growing attention from both the research community and medicine industry. It addresses the problem of diagnosis, analysis and teaching purposes in medicine. For these several medical imaging modalities and applications based on data mining techniques have been proposed and developed. Thus, the primary objective of medical images classification is not only to achieve good accuracy but to understand which parts of anatomy are affected by the disease to help clinicians in early diagnosis of the pathology and in learning the progression of a disease. This furnishes motivation from the advancement in data mining techniques and particularly in soft set, to propose a classification algorithm based on the notions of soft set theory. As a result, a new framework for medical imaging classification consisting of six phases namely: data acquisition, data pre-processing, data partition, soft set classifier, data analysis and performance evolution is presented. It is expected that soft set classifier will provide better results in terms of sensitivity, specificity, running time and overall classifier accuracy

A Genetic Algorithm Based Feature Selection for Classification of Brain MRI Scan Images Using Random Forest Classifier

A brain tumour is a mass of tissue that is formed by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Magnetic Resonance Imaging is a useful imaging technique that is widely used by physicians to investigate different pathologies. After a long clinical research, it is proved to be harmless. Improvement in computing power has introduced Computer Aided Diagnosis (CAD) which can efficiently work in an automated environment. Diagnosis or classification accuracy of such a CAD system is associated with the selection of features. This paper proposes an enhanced brain MRI image classifier targeting two main objectives, the first is to achieve maximum classification accuracy and second is to minimize the number of features for classification. Feature selection is performed using Genetic Algorithm (GA) while classifiers used are Random forest Classifier