Curvelet Transform Based Brain Tumor Classification Using Soft Computing Techniques

Abstract

In this project, a new method for Brain Tumor Classification using Probabilistic ANFIS Classifier is proposed. The conventional method for computerized tomography and magnetic resonance brain images classification and tumor detection is by human inspection. Operator assisted classification methods are impractical for large amounts of data and are also non reproducible. Computerized Tomography and Magnetic Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies in classification. Various features such as local binary pattern (LBP) and law's texture features are extracted from brain MRI images. These feature set are trained and classified by using Support vector machine (SVM) and ANFIS (Adaptive neuro fuzzy interference system) Classifier. This algorithm is independent of the tumor type in terms of its pixels intensity. The proposed method gives fast and better recognition rate when compared to previous classifiers. The main advantage of this method is its high speed processing capability and low computational requirements.