The geographical conditions of Colombia favor the re-emergence and propagation of infectious tropical diseases. Among them, the Dengue virus is highly endemic throughout the country, thereby locating this arbovirus as one of the major pathologies of the public health system. Therefore, there is a global challenge to generate novel strategies to predict and control dengue virus transmission. In particular, during the Colombian 2016 Dengue outbreak, more than 17 thousand Dengue cases were reported by the health authorities of Medellin, Antioquia. In this paper, we present a machine learning approach for the early detection of dengue outbreaks in the city of Medellin. We use an artificial neural network as the core of the machine learning algorithm, with environmental, meteorological and epidemiological data from the National Institute of Health –SIVIGILA– and the Aburra Valley Early Warning System –SIATA–. Our objective is to identify possible Dengue outbreaks, i.e. to create an early warning system, to provide a preventive timeline to the health authorities to design contingency plans and to mitigate the impact of dengue on the population of Medellin. Our results indicate that a artificial neural network forecasting for time series shows a trend for the correct prediction of dengue cases up to the first four weeks with a deterioration in precision as the forecast is pushed for additional ten to twenty weeks.
In this work, we present a shape-based approach to automatic skin lesion segmentation and classification in dermoscopic images. We aim to differentiate three types of lesion 1) common nevi, 2) atypical nevi, and 3) melanomas by exploring the morphology features of segmented skin lesions. Our method is an attempt to design a computer-aided ABCDEs of melanoma, where the Asymmetry and Border components are estimated using morphological features. The lesions are first segmented using a super-pixel merging strategy with an RGB criterion. Later, the segmentation method was evaluated on the PH2 dataset, and compared with other state-of- the-art skin segmentation methods. The classification was also conducted on the PH2 dataset through a 10-fold cross-validation set-up with a training and testing set partition of 90% and 10% respectively. We employed logistic regression, SVM and a neural network as classification algorithms. The best performances was 86.5% on average with the neural network.
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