Detection of Tumour in MRI Images of Brain Using Spearman Algorithm

Abstract

This project investigates the problem of image classification with limited or no annotations, but abundant unlabeled data. The setting exists in many tasks such as semi-supervised image classification, image clustering, and image retrieval. Unlike previous methods, which develop or learn sophisticated regularizes for classifiers, our method learns a new image representation by exploiting the distribution patterns of all available data. Particularly, a rich set of visual prototypes are sampled from all available data, and are taken as surrogate classes to train discriminative classifiers; images are projected via the classifiers; the projected values, similarities to the prototypes, are stacked to build the new feature vector. The training set is noisy. Hence, in the spirit of ensemble learning, we create a set of such training sets which are all diverse, leading to diverse classifiers i.e. Deep learning neural network and RBFNN classifier. It is conceptually simple and computationally efficient, yet effective and flexible. This project is implemented using the Matlab simulation.

Keywords: Brain Tumour, Radial Basis Function Neural Network, Spearman Algorithm