Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer

We compared three common models: paragraph vectors, convolutional neural networks (CNN) and recurrent neural networks (RNN), and conventional machine-learning methods.. We demon-

Recently, new approaches based on Deep Learning have demonstrated good capacities to tackle Natural Language Processing problems, such as text classification and information

This study primarily aims to address terrain classifica- tion by utilising raw time-series vibration data as input to three implemented deep learning frameworks: LSTM, CNN, and

The main purpose of the proposed study was to improve the overall accuracy level of two state of art deep learning networks which include Inception-v3 and MobileNet by using

Nevertheless, the main goal of this paper is to explore deep learning approaches using convolutional [6], recurrent [8] and self-attention networks [11] (CNNs, RNNs, and SANs) to

In this competition, for segmentation (task 2) of five types of artefact, patch-based fully convolutional neural network (FCN) allied to support vector machine (SVM) classifier

Recently, new approaches based on Deep Learning have demonstrated good capacities to tackle Natural Language Processing problems, such as text classification and information

To tackle the first challenge, based on our previous experience [3], we rely on features extracted through four different Convolutional Neural Network (CNN) models pre-trained on