CapsNet topology to classify tumours from brain images and comparative evaluation

Visual evaluation of many magnetic resonance images is a difficult task. Therefore, computer-assisted brain tumor classification techniques have been proposed. These techniques have several drawbacks or limitations. Capsule based neural networks are new approaches that can preserve spatial relationships of learned features using dynamic routing algorithm. By this way, not only performance of tumor recognition increases but also sampling efficiency and generalisation capability improves. Therefore, in this work, a Capsule Network (CapsNet) is used to achieve fully automated classification of tumors from brain magnetic resonance images. In this work, prevalent three types of tumors (pituitary, glioma and meningioma) have been handled. The main contributions in this paper are as follows: 1) A comprehensive review on CapsNet based methods is presented. 2) A new CapsNet topology is designed by using a Sobolev gradient-based optimisation, expectation-maximisation based dynamic routing and tumor boundary information. 3) The network topology is applied to categorise three types of brain tumors. 4) Comparative evaluations of the results obtained by other methods are performed. According to the experimental results, the proposed CapsNet based technique can achieve extraction of desired features from image data sets and provides tumor classification automatically with 92.65% accuracy.