Automated Classification of Multiclass Brain Tumor MRI Images using Enhanced Deep Learning Technique

Abstract

The brain is a vital organ, and the brain tumor is one of the most dangerous types of tumors in the world. Neuroimaging is an interesting and important discussion in diagnosing central nervous system tumors. Brain tumors have several types, namely meningioma, glioma, pituitary, schwannoma, and neurocytoma. A radiologist uses magnetic resonance imaging (MRI) to detect brain tumors because of its advantages over computed tomography. However, classifying multiclass MRI is difficult and takes a long time. This study proposes an automated classification of multiclass brain tumors using enhanced deep learning techniques. Various models are used in this research, namely VGG16, NasNet-Mobile, InceptionV3, ResNet50, and EfficientNet. For EfficientNet, we applied EfficientNet-B0-B7. From the experiments, EfficientNet-B2 is the superior, with the highest level of training accuracy of 99.90%, testing accuracy of 99.55%, precision of 99.50%, recall of 99.67%, and F1-Score of 99.58% with a training time of 15 minutes. The development of this automatic classification can assist radiologists in classifying brain tumor types more efficiently.