Classification of LGG/GBM Brain Tumor in MRI Using Deep-Learning Schemes: A Study

Brain abnormalities require immediate medical attention, including diagnosis and treatment. One of the most severe brain disorders is brain tumor, and magnetic resonance imaging (MRI) is frequently used for clinical level screening of these illnesses. In order to categorize brain MRI images into low-grade gliomas (LGG) and glioblastoma-multiform (GBM), a deep learning strategy will be implemented in this work. The steps in this scheme are as follows: (i) gathering data and converting 3D to 2D; (ii) deep features mining using selected scheme; (iii) binary classification using SoftMax; and (iv) comparison analysis using selected deep learning techniques to determine the best model for additional refinement. The LGG/GBM photos are thought to be gathered by the Cancer Imaging Archive (TCIA) database. The results of this study demonstrate that max-pooling offers a higher accuracy than average-pooling based models, and the performance of the created scheme is validated using both average- and maxpooling. In the chosen models, the result of VGG16 is superior for the LGG/GBM detection task.