MRI Brain Tumor Classification Using Computational Intelligence Technique

Abstract

We proposed a method for detecting and classifying tumors using parallel processing, soft computing, and machine learning techniques. Due to the augmented size and capacity of medical images, the medical imaging field must need an automatic diagnosis process for tumor classification for improved treatment. Initially, we pre-process Magnetic Resonance Imaging (MRI) brain tumor images using non-local adaptive means filter and contrast enhancement in the proposed system. We use a Fuzzy Clustering Means (FCM) algorithm to segment the tumors from the preprocessed MRI brain images. The significant features are obtained from the segmented tumor images by using Discrete Wavelet Transform (DWT), Principal Components Analysis (PCA), and the gray level co-occurrence matrix (GLCM). Machine learning classifiers are peerless techniques for classifying tumors. The classification of tumor grade is done through the Multiclass Support vector machine (MCSVM) classifier. The entire proposed work is implemented on the NVIDIA GeForce GTX parallel processing environment. The innovative final result of our approach was assessed by the Kaggle dataset with the performance metrics and achieved 98.056 % Sensitivity, 100 % Specificity, and 98.139 % Accuracy in 0.3337 computational times