Experimenting with Extreme Learning Machine for Biomedical Image Classification

Abstract

Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently emerged which, as shown in experimental studies, can produce an acceptable predictive performance in several classification tasks, and at a much lower training cost compared to deep learning networks that are trained by backpropagation. We propose a method devoted to exploring the possibility of considering extreme learning machines for biomedical classification tasks. Binary and multiclass classification in four case studies are considered to demonstrate the effectiveness of extreme learning machine, considering the biomedical images acquired with the dermatoscope and with the blood cell microscope, showing that the extreme learning machine can be successfully applied for biomedical image classification.