An augmented customized deep learning approach for brain tumour identification

Abstract

ABSTRACT Brain is one of the crucial organs in the human body and the survival rate for those who are affected by brain tumours across the globe is very low. There might be low survival rate yet it can be improved by identifying the disease by using MRI of the brain at a very early stage. At this juncture, the automatic identification of the tumour accurately using MRI images is very essential. Once the deep learning came into existence, the accuracy of identification of various biomedical diseases using MRI or X-ray images has been improved.Existing Systems are using deep learning approach identification of the diseases but they are lacking in the size of the data which they are using for the implementation purpose. Considering this aspect as an inspiration, a framework proposed for the identification of brain tumour with a customized neural network along with a CNN architecture. The dataset considered for the implementation of this framework is an open dataset obtained from Kaggle. As the dataset is smaller, the data augmentation technique is used to improve the dataset size. So, the effect of data augmentation in attaining the accuracy is also discussed. The training accuracy obtained during pre-data augmentation is about 85.76% and post-data augmentation is about 97.85%.