Automated Classification of Brain Tumor Disease with a Novel CNN Relief and SVM-Based Deep Hybrid Model

Abstract

The brain tumor is a very dangerous type of cancer that can be seen in people of almost any age and usually results in the patient's death. Early detection of these tumors, which have many varieties, is extremely important in terms of the patient's survival, affecting the planning of treatment, just as with other types of cancer. Early diagnosis of the disease is usually performed by means of imaging devices. It takes a lot of expertise to analyze the MRI images and diagnose the brain tumor. In this study, a hybrid deep model is recommended that can be used effectively in the classification of the brain tumor. The proposed hybrid model is a Convolutional Neural Network (CNN)-based method that automatically classifies Magnetic Resonance (MR) images of three different types of brain tumors, Glioma, Meningioma and Pituitary successfully. Our model is basically going through these stages. First of all, the features from the two models that show the highest performance from pre-trained deep models are combined. The most effective features of the specification map obtained in the next phase were selected using the Relief method. At the last stage, classification was carried out with Support Vector Machine (SVM), one of the most known machine learning techniques. As a result of the experiments, the hybrid deep model we proposed obtained 93.2% accuracy. It seems that proposed hybrid method has very competitive results and is thought to be efficiently used to classify the brain tumor.