Deep Online Sequential Extreme Learning Machines and its Application in Pneumonia Detection

摘要

Deep neural networks have demonstrated high levels of accuracy in the fields of image classification. Deep learning is a multilayer perceptron artificial neural network algorithm, that uses a backpropagation based learning technique to approximate complicated functions and alleviating the difficulty associated with optimizing deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. Furthermore, Online Sequential Extreme Learning Machines (OSELM) is an adaptive algorithm based on ELM that does not require fresh training when faced with a new dataset, but can adapt to the new dataset by being trained on the new dataset alone. We combining MLELM and OSELM put forward Multilayer OSELM and apply it to the Pneumonia Chest X-Ray image dataset in this paper. By simulating and analysing the results of the experiments, effectiveness of the application of Multilayer OSELM in Pneumonia identification is confirmed.