Convolutional Neural Network-Based MRI Image Analysis for Meningioma and Glioblastoma Brain Tumor Classification

Abstract

The lack of radiological equipment/personnel and difficulties in acquiring licensure for MRIs and CT scans in provinces in far flung areas of the Philippines remain to be a hindrance in timely medical intervention. Additionally, development of a non-invasive tool for initial diagnosis and classification of brain tumors is of interest due to its high prevalence. CNN-based MRI image analysis aims to classify between meningioma and glioblastoma brain tumor types and between the presence and absence of brain tumors. The study obtained glioblastoma, meningioma, and normal brain MRIs from publicly available online databases for training, verification, and validation. After training, comparisons of model loss and accuracy were done with Adam, SGD, RMSProp, and Adadelta. SGD was found to be the most effective optimizer in normal and brain tumor classification (validation accuracy of 87.00%) while RMSProp was the most effective for normal, meningioma, and glioblastoma classification (validation accuracy of 96.33%). Various factors such as increased image dataset size, number of subjects, number of slices for each category, and types of brain tumor is recommended for future studies. Furthermore, the investigators recommend including images from Philippine hospitals in training classification while setting ample training time as pre-trained models would benefit from a longer training time.