Detection of Brain Tumor using Image Classification

One of the most common diseases in India is Brain tumor, which is spreading due to many reasons, most common reason is identified as Iifestyle of people. But, with the changing trends and technology, the identification and treatments are also increasing only if early detected. Early detection of any disease will help in better treatment. The image processing techniques help in detecting the tumor images at an early stage. With the help of the scanned MRI images it is possible to detect the tumor and it's severity. In this paper, we propose the system to classify the images into two groups, Malignant or Benign. The proposed system is based on second order texture features and SVM classifier. Various second order features like Energy, Entropy, Homogeneity and correlation are used to build the system. The work is carried in the following steps, preprocessing which includes feature extraction followed by training the images on SVM classier based on the extracted features and finally testing on the SVM classier with various kernels. With Linear kernel, highest sensitivity, specificity and accuracy obtained are 80%, 90% and 80% respectively. The results of the work are to classify an image with tumor as Malignant or Benign. The results obtained illustrate the robustness of the system in identifying and classifying the Brain tumor.