Use of 3D Coronal and Sagittal Images to Improve the Diagnosis of Brain Tumor

Abstract

A brain tumour is a type of cancer which is very hard to detect by a doctor in the starting stages. Generally, the shape and size of the tumour are unidentified. The Brain tumour classification is performed by serologic analysis and is not usually conducted before conclusive brain surgery. Normally Brain tumour is predicted by Magnetic Resonance Imaging (MRI) images, however, it is time-consuming and high cost. Nowadays a lot of data sets are available for identifying the several stages of brain tumour such as Glioma, Meningioma and a Pituitary tumour to train the Machine Learning (ML) model. The conventional ML models logistic regression, support vector machine (SVM), Convolution Neural Network (CNN) and Residual Neural Network (RNN) are used to predict the location of tumour present in the brain and also able to create tumour pattern mask. However, their accuracy is very less. In this paper, an effective ML model 3D U-NET has been developed that can generate a tumour pattern mask for any type of tumour present in the brain. The proposed model provides better accuracy as compared to the conventional method. Simulation results shows that, 85% accuracy.