Binary Classification of Meningioma Grades Using CNN and VGG16 + XGBoost Deep Learning Models

Meningioma, a common type of brain tumor, highlights the critical need for early detection and treatment. The proposed research work addresses the severity level of meningial brain tumors by classifying the meningioma grades. The proposed work makes use of a dataset containing 35 Grade II and 49 Grade I images. To achieve accurate binary classification of meningioma grades, two deep learning models SimpleCNN and VGG16 + XGBoost were proposed. The proposed models use MRI image dataset to train the SimpleCNN model, allowing it to directly extract pertinent characteristics from the images. VGG16 model is a pretrained model used for extracting complex characteristics from the images in the MRI dataset. Then, the VGG16 model is integrated with the XGBoost classifier for the classification of meningioma grades. By comparing the performance of two models, VGG16 + XGBoost model outperforms the simple CNN, with an accuracy of 98.4%, in binary classification tests.