Analyzing Digital Image by Deep Learning for Melanoma Diagnosis

Image classi cation is an important task in many medical applications, in order to achieve an adequate diagnostic of di erent le- sions. Melanoma is a frequent kind of skin cancer, which most of them can be detected by visual exploration. Heterogeneity and database size are the most important di culties to overcome in order to obtain a good classi cation performance. In this work, a deep learning based method for accurate classi cation of wound regions is proposed. Raw images are fed into a Convolutional Neural Network (CNN) producing a probability of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were used due to their well-known e ectiveness. Moreover, data augmentation was used to increase the number of input images. Experiments show that the compared models can achieve high performance in terms of mean ac- curacy with very few data and without any preprocessing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Computational investigation of diesel nozzle internal flow during the complete injection event

[EN] Currently, diesel engines are calibrated using more and more complex multiple injection strategies. Under these conditions, the characteristics of the flow exiting the fuel injector are strongly affected by the transient interaction between the needle, the sac volume and the orifices, which are not yet clear. In the current paper, a methodology combining a 1D injector model and 3D-CFD simulations is proposed. First, the characteristics of the nozzle flow have been experimentally assessed in transient conditions by means of injection rate and momentum flux measurements. Later, the 3D-CFD modeling approach has been validated at steady-state fixed lift conditions. Finally, a previously developed 1D injector model has been used to extract the needle lift profiles and transient pressure boundary conditions used for the full-transient 3D-CFD simulations, using adaptive mesh refinement (AMR) strategies to be able to simulate the complete injection rate starting from 1 mu m lift.This work was partly sponsored by "Ministerio de Economia y Competitividad'', of the Spanish Government, in the frame of the Project "Estudio de la interaccion chorro-pared en condiciones realistas de motor'', Reference TRA2015-67679-c2-1-R. The authors would like also to thank the computer resources, technical expertise and assistance provided by Universidad de Valencia in the use of the supercomputer "Tirant''. 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