Complete Blood Cell Detection and Counting Based on Deep Neural Networks

Deep Convolutional Neural Networks for Human Embryonic Cell Counting

We address the problem of counting cells in time-lapse microscopy images of developing human embryos. Cell counting is considered as an important step in analyzing biological phenomenon such as embryo viability. Traditional approaches to counting cells rely on hand crafted features and cannot fully take advantage of the growth in data set sizes. In this paper, we propose a framework to automati...

Bounding and Counting Linear Regions of Deep Neural Networks

In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions, based on PWL activation units such as rectifier or maxout. We investigate the complexity of such networks by studying the number of linear regions of the PWL function. Typically, a PWL function from a DNN can be seen as a large family of linear function...

Deep neural networks and fraud detection

extracting, recognizing, and counting white blood cells from microscopic figures by using complex-valued neural networks

in this paper we present a method related to extracting white blood cells (wbcs) from blood microscopic figures and recognizing them and counting each kind of wbcs. in this method, first we extract the white blood cells from other blood cells by rgb color system's help. in continuance, by using the features of each kind of globules and their color scheme, we extract a normalized feature vector,...

Exploiting deep neural networks for detection-based speech recognition

In recent years deep neural networks (DNNs) – multilayer perceptrons (MLPs) with many hidden layers – have been successfully applied to several speech tasks, i.e., phoneme recognition, out of vocabulary word detection, confidence measure, etc. In this paper, we show that DNNs can be used to boost the classification accuracy of basic speech units, such as phonetic attributes (phonological featur...