Estimación de parámetros en modelos epidemiológicos de VIH/SIDA.

The validation process of mathematical models that describe practical applications usually implies the estimation of the unknown parameters that are involved. In this work, in order to estimates these parameters in the HIV/AIDS models of [7], the problem of estimatingthe parameters in first order ordinary differential equations with known start point is formulated and a strategy of solution is presented. It is verified as well, which is the model of HIV/AIDS that represents best the real data according with the strategy of solution.El proceso de validación de modelos matemáticos que describen aplicaciones prácticas implica, generalmente, la estimación de los parámetros desconocidos que en ellos intervienen. En este trabajo, con el fin de estimar estos parámetros en los modelos de VIH/SIDA de [7], se formula el problema de estimación de parámetros en ecuacionesdiferenciales ordinarias de primer orden con punto inicial conocido y se presenta una estrategia de solución al mismo. Se verifica, además, cuál es, en algún sentido, el modelo de VIH/SIDA que mejor representa los datos reales existentes según la estrategia de solución

Estimación de parámetros en modelos epidemiológicos de VIH/SIDA.

The validation process of mathematical models that describe practical applications usually implies the estimation of the unknown parameters that are involved. In this work, in order to estimates these parameters in the HIV/AIDS models of [7], the problem of estimatingthe parameters in first order ordinary differential equations with known start point is formulated and a strategy of solution is presented. It is verified as well, which is the model of HIV/AIDS that represents best the real data according with the strategy of solution.El proceso de validación de modelos matemáticos que describen aplicaciones prácticas implica, generalmente, la estimación de los parámetros desconocidos que en ellos intervienen. En este trabajo, con el fin de estimar estos parámetros en los modelos de VIH/SIDA de [7], se formula el problema de estimación de parámetros en ecuacionesdiferenciales ordinarias de primer orden con punto inicial conocido y se presenta una estrategia de solución al mismo. Se verifica, además, cuál es, en algún sentido, el modelo de VIH/SIDA que mejor representa los datos reales existentes según la estrategia de solución

Diabetic foot ulcer segmentation using logistic regression, DBSCAN clustering and mathematical morphology operators

Digital images are used for evaluation and diagnosis of a diabetic foot ulcer. Selecting the wound region (segmentation) in an image is a preliminary step for subsequent analysis. Most of the time, manual segmentation isn't very reliable because specialists could have different opinions over the ulcer border. This fact encourages researchers to find and test different automatic segmentation techniques. This paper presents a computer-aided ulcer region segmentation algorithm for diabetic foot images. The proposed algorithm has two stages: ulcer region segmentation, and post-processing of segmentation results. For the first stage, a trained machine learning model was selected to classify pixels inside the ulcer's region, after a comparison of five learning models. Exhaustive experiments have been performed with our own annotated dataset from images of Cuban patients. The second stage is needed because of the presence of some misclassified pixels. To solve this, we applied the DBSCAN clustering algorithm, together with dilation, and closing morphological operators. The best-trained model after the post-processing stage was the logistic regressor (Jaccard Index $0.81$, accuracy $0.94$, recall $0.86$, precision $0.91$, and F1 score $0.88$). The trained model was sensitive to irrelevant objects in the scene, but the patient foot. Physicians found these results promising to measure the lesion area and to follow-up the ulcer healing process over treatments, reducing errors