Performance Evaluation of Feature Extraction and SVM for Brain Tumor Detection Using MRI Images

The aim of this study is to develop an automatic detection of brain tumors from magnetic resonance images based on artificial intelligence.The developed approach comprises three steps: pre-processing, feature extraction, and classification.The pre-processing consists of applying image processing techniques to improve contrast and reduce noise in magnetic resonance images.The feature extraction consists of transforming magnetic resonance images into numerical vectors that represent the discriminating attributes for tumor detection.Then the classification consists of using a machine-learning algorithm to separate magnetic resonance images into two classes: tumoral and non-tumoral.The performance evaluation of the proposed approach is tested under dataset of 3000 magnetic resonance images, where 1500 magnetic resonance images are with tumors and 1500 magnetic resonance images are without tumors.In the feature extraction step, two techniques have been used the bag of features and the ResNet50 convolution neural network then a comparison between them was performed.In the last step, the obtained images have been compared with the three different kernels function for the support vector machine classifier: Linear, Quadratic, and Cubic.The proposed magnetic resonance images classification approach was tested using confusion matrices and receiver operating characteristic curves, which revealed satisfactory performance in terms of Sensitivity, Precision, Specificity and Accuracy.The obtained results show that the BoF-SVMs combination achieves the best classification accuracy, with a recognition rate of 100%.