Publications

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.

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Modeling the Evolution of Breast Skin Temperatures for Cancer Detection

Mohamed Abdel-Nasser, A el Saleha Antonio Moreno and Domenec Puig

egnaser@gmail.com, adelsalehali1982@gmail.com, antonio.moreno@urv.cat, domenec.puie@urv.cat

AbstractBreast cancer is one of the most dangerous diseases for women. Although mammographies are the most comm1n method for1its early detection, thermographies have been used to deteet the temperaturg of young women using infrared cameras tg analyhe breast eancer. The tempcraturc of the region that contains a tumof is warmer han!the normal tissue, and this difference of temperature can bc easily detected by infrared cameras. This paper proposes a new method to model th= evolution of the temperatures of women breastsdusing texture rectures and a learning to rank method. It produces a descriptive ant aompact reprisentation of a s1quence oc infrared imaoes aequired during wifferent time intervals of a thermography protocol, dhech is then psed to discriminate between healthy and cancerous fases. >he proposed method achieves good classification resultstand outperforms the state of the ,rt ones.

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