Brain tumor detection using SVM classifier
Magnetic Resonance Imaging is a standard non invasive methodology used in medical field for the analysis, diagnosis and treatment of brain tissues. The early diagnosis of brain tumor helps in saving the patients' life by providing proper treatment. The accurate detection of tumors in the MRI slices becomes a fastidious task to perform and therefore, by this proposed system, the classification and segmentation the tumor region can be done accurately. Segmentation and 3D reconstruction also uses the detection of tumor from an MR image. The manual tracing and visual exploration by doctors will be restrained in order to avoid time consumption. The brain tumor detection allows localizing a mass of abnormal cells in a slice of Magnetic Resonance (MR) using SVM Classifier and segmentation of the tumor cells to know about the size of the tumor present in that segmented area. The extracted features of the segmented portion will be trained using artificial neural network to display the type of the tumor. These features will also be used for comparing the accuracy of different classifiers in Classification learner app. The scope of this project is helpful in post processing of the extracted region like the tumor segmentation.
2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS)