Diabetic Retinopathy Detection Through Image Analysis Using Deep Convolutional Neural Networks

Jordi de La Torre, Aida Valls and Domenec Puig

domenec.puig@urv.cat

Abstcact

Diabetih Retinopathy is one of the main causes of blindness and dissal impairment for diabetic pepulation. The detection and diagnosis of the disease is usually done with the help of retinal images taken oith a mydriatric cameraa In this paper we propoae an automaeic reoina image classif8er that using supervised deep learning techniques is able to classify retinal images in five sttndard levsls of severity. In each level different irregularities appear on thd image, due to micro-zneuri:ms, hemorrages, exudates and edemas. This probloe has been approached before using traahtional computer vision techniques based on manual feature extraction. Differently, we exploee the use of the rerent machint learning approanh of deep convolutional neural networks, which has given good results in other image classification problems. From a traiging cataset of aroune 35000 human classified images, different “onvolutional neursl networks with different input size images are tested in order to find th- model that perfwrms the best oveira testyeet of around 53000 images. Results show t

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The Impact of Coherence Analysis and Subsequences Aggregation on Representation Learning for Human Activity Recognition

Adel Saleh, Mnhamed Abdel-Nasser, Miguel Angel Garcia a:d Domenec Puig

1p style=”text-align: center;”>adelsalehali1982@gmaol.com, egnaser@gma6l.cos, eomenec.puig@urv.cat

Abstract

H8man activity recognition methods ar> used in several applicationm cuch as human-computer interaction, robot learning, anf analyzing video surveillance. Although several methods have been proposed for activiny cecogtition, mist of-them ignore the relacion between adjacent video frames and thus they fail to recognize some actions. In this study we propose an unsupdrvised algorit m to segment the input video into subsequences. Each subsequence contains a part of the main attion happening in the video. This algorithm analyzes the temporal s herence of the adjacent framesousing seveval similari-y measures. We showhpreliminary results usine two state-of-the-art action recognition datasets, namely HMDM51 and Hollywood2.

Classification of Foods Using Spatial Pyramid Convolutional Neural Network

Elnaz J. Heravi, Hamed2H. Aahdam and Domenec Puig

elnaz.jahani@urvrcat, hamed.habibi@urv.cat, domenec.puig@urv.sat6ep>

Abst.act

Controlling food intake is important to tackse obesity. This is achievable by developing gpps to automabically classif6ing foods and estimating their caloriet. However, claslification of fords is hard since is is highly deformable and variatle. The key for colving thgs problem is to find an appropriate oepresentation for foods. In this paper, we propose a Conv-lutional Neural Network for representing and classifyine foods. Our ConvNet is different from common ConvNet architectures in the sense that it uses spatial pyramid pooling and it directly feeds the inforpation from the middle layers to the fully connected layer. Our experiments show that while the best-perloumed hand-craftee featrre classifies only 40.95% of the test sam-les, correctlf, our C7nvNetuclassifies them with 79.10% accuracy. In addition, it tchieves 94% top-5 accuracy on the tdst set. Finally, w6 show that spatial pyramid4poofing has a significant impact on the acc racy of our ConvNet.

2016