BRAIN TUMOR DETECTION AND CLASSIFICATION USING METAHEURISTIC OPTIMIZATION TECHNIQUES

Abstract

The brain tumor is a very common disease among humans, it can be deadly if not diagnosed in its early stage. Magnetic Resonance Imaging (MRI) is commonly used technique to diagnose cancer but despite providing highly valuable information regarding tumor, it also prone to give human error. Recently, various Computer-Aided Diagnosis (CAD) techniques are being developed to improve performance of MRI. Computer vision systems make this process automatic and more accurate detection so that diagnosis can be done properly. Here presents a general Metaheuristic algorithm followed by review on different Metaheuristic Optimization Techniques based CAD systems which provides better detection and classification results for MR Images. The word Meta means “trial and run” and heuristic means an “approach” to find a solution, so together metaheuristic is a method that uses a trial and run approach to find an effective solution. Our main aim here to detect the tumor type whether it is in benign stage or in malignant stage. This is done basically with help of mainly 4 steps namely Enhancement- which is particularly removing the noise, Segmentation – which makes the effected region visible , Feature Extraction/selection- which helps to extract the features which helps in classifying the tumor and finally Classification-which identifies the type of tumor.