An Effective CNN Method Using Multi-SVM Process for Brain Tumor Segmentation and Detection from MR Images

In most of the medical applications, the accuracy of detecting and diagnosing the disease in a proper procedure is always a challenging issue. One of the most searched research works is brain tumor detection with most effective way; here, deep learning-based algorithms yield better outcomes. Brain tumor segmentation algorithms have been more investigated in the recent times with varied algorithms for accurate segmentation and detection. In this work, brain tumor detection from the MR scan images is mainly based on convolution neural network (CNN) amalgamated with multi-SVM classifier with various preprocessing and intermediate steps involved to bring out optimal results. In preprocessing stage, image filtering and intensity normalization of input images are carried out. At later stages, CNN along with multi-SVM classifier is utilized for training, testing along with classification process. Finally, the classification tends to display the presence of brain tumor or not. The implementation is processed by means of MATLAB computing language with necessary prerequisites.