12 research outputs found
Assessing hyper parameter optimization and speedup for convolutional neural networks
The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures
Obtaining deep learning models for automatic classification of leukocytes
In this work, the authors classify leukocyte images using the neural network architectures that won the
annual ILSVRC competition. The classification of leukocytes is made using pretrained networks and
the same networks trained from scratch in order to select the ones that achieve the best performance for
the intended task. The categories used are eosinophils, lymphocytes, monocytes, and neutrophils. The
analysis of the results takes into account the amount of training required, the regularization techniques
used, the training time, and the accuracy in image classification. The best classification results, on the
order of 98%, suggest that it is possible, considering a competent preprocessing, to train a network like
the DenseNet with 169 or 201 layers, in about 100 epochs, to classify leukocytes in microscopy images.info:eu-repo/semantics/publishedVersio