Deep learning based brain tumor segmentation and classification using MRI images

Abstract

Following an MRI scan of a patient, radiologists partition the tumor-bearing area of the brain based on their previous experience. The presence of cerebrospinal fluid and white matter in the brain makes it difficult to pinpoint the tumor's location. Human observation is prone to error, especially when performed by a radiologist with less experience segmenting the MRI image. They are manually classified after segmentation based on the tumor's growth rate, origin area, and harmfulness. Deep learning models can be used to separate and classify data efficiently. Classification can be done before or after segmentation; here, segmentation is performed using U-Net while classification is done utilising some of the most efficient CNN models: VGG16, Resnet50, Inception V3, and SqueezeNet. After noise removal and data augmentation, Resnet50 outperforms the other four pretrained CNN models for the classification of MRI images on the "Cjdata" dataset (also called the "Brain Tumor" dataset).