Identification of Cancerous Cell from Noisy Images

Abstract

Over the last decades, the incidence of skin cancer, melanoma and non-melanoma,has increased at a continuous rate. In particular for melanoma, the deadliest type of skin cancer, early detection is important to increase patient prognosis. Recently, deep neural networks (DNNs) have become viable to deal with skin cancer detection. In this work, we present a smartphone-based application to assist on skin cancer detection. This application is based on a Convolutional Neural Network (CNN) trained on clinical images and patients demographics, both collected from smartphones. Also, as skin cancer datasets are imbalanced, we present an approach, based on the mutation operator of Differential Evolution (DE) algorithm,to balance data. In this sense, beyond provides a flexible tool to assist doctors on skin cancer screening phase, the method obtains promising results with a balancedaccuracy of 85% and a recall of 96%. Index Terms—skin cancer detection, smartphone application, deep learning, convolutional neural network

Numerical investigation on erosion wear and strength of main gas pipelines bends

“…Since it is impossible to obtain an analytical solution, numerical modeling can be used to establish quantitative relationships in modern software systems, which is an effective tool for solving such problems, both in a complex (multidisciplinary modeling) and partially. Visualized results of such modeling allow to see in detail a complex three-dimensional single-phase or multiphase flow inside various shaped elements of gas pipeline systems [19][20][21][22] and to study the distribution of pressure, flow velocity, kinetic energy of turbulence, to determine the trajectories of dispersed phases in the continuous phase, the location and rate of erosive wear of the wall, etc. The results of numerical modeling also help to create three-dimensional models of shaped elements of gas pipelines with erosional defects of complex structure, which will occur at certain intervals of pipeline operation.…”