Detection of brain tumor using several convolutional neural network architectures

Abstract

The word "brain tumor" defines the unusual expansion of the cells in the brain. Among other tumors, brain tumors are possibly one of the most alarming and lifethreatening. So, detection of brain tumor in early stage is much needed because many individuals died as they were unaware of getting a tumor in the brain. For this purpose, di erent machine learning algorithms and image processing techniques are used for the early detection of brain tumor. The aim of this study is to detect brain tumor by observing di erent areas of brain and tumorous grow of brain tissues with the help of functional magnetic resonance imaging (fMRI) data. Our main goal is to determine whether the tumor is present in patient's brain or not. After data collection, we have pre-processed the data where di erent steps like image extraction, data segmentation were performed. We have used CNN architectures for the classi cation of brain tumor. For this purpose, di erent pre-trained CNN model VGG16, VGG19, Inception V3, ResNet50, DenseNet121 and Xception have implemented. Among those models we have identi ed 3 models (Inception V3, DenseNet121, VGG19) which gave higher accuracy compared to other models and selected them for further work. Rather than taking one model as most accurate we have used ensemble method in our study which produced better predictive solution in terms of brain tumor detection.