Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images

Abstract

Automatic binary classification of brain magnetic resonance (MR) images has made remarkable progress in the past decade. In comparison, a few pieces of work has been reported on multiclass classification of brain MR images. However, there exist enough scopes for improved automation and accuracy. Most of the existing schemes follow the multi-stage pipeline structure of conventional machine learning framework, where the features are designed manually or hand-crafted. In recent years, deep learning models have attracted great interest from researchers for analyzing medical images that eliminate the traditional steps of machine learning. In this paper, we present an automated method based on deep extreme learning machine (ELM) also termed as multilayer ELM (ML-ELM) for multiclass classification of the pathological brain. ML-ELM is a multilayer architecture stacked with ELM based autoencoders. The effectiveness of leaky rectified linear unit (LReLU) activation function is investigated with ML-ELM. Extensive simulations on a multiclass brain MR image dataset indicate that the ML-ELM with LReLU activation (ML-ELM+LReLU) achieves higher performance with faster training speed compared to its counterparts as well as state-of-the-art schemes. The basic purpose of employing ML-ELM+LReLU algorithm is to eliminate the need for hand-crafted feature extraction and to develop a more stable and generalized system for multiclass brain MR image classification.