Comparison Review on Brain Tumor Classification and Segmentation using Convolutional Neural Network (CNN) and Capsule Network

Abstract

Malignant brain glioma is considered as one of the deadliest cancer diseases that has a higher fatality rate than the survival rate. In terms of brain glioma imaging and diagnosis, the processes of detection and segmentation are manually done by the experts. However, with the advancement of artificial intelligence, the implementation of these tasks using deep learning provides an efficient solution to the management of brain glioma diagnosis and patient treatment. Deep learning networks are responsible for detecting, segmenting, and interpreting the tumors with high accuracy and repeatability so that the appropriate treatment planning can be offered to the patient. This paper presents a comparison review between two state of the art deep learning networks namely convolutional neural network and capsule network in performing brain glioma classification and segmentation tasks. The performance of each published method is discussed along with their advantages and disadvantages. Next, the related constraints in both networks are outlined and highlighted for future research.