Roberto F. Ivo

TL;DR: This work proposes the use of Transfer Learning and Deep Learning in an IoT system to assist doctors in the diagnosis of common skin lesions, typical nevi, and melanoma, and uses Convolutional Neural Networks (CNNs) as resource extractors.

TL;DR: A new approach to the segmentation of cardiac fat from Computed Tomography (CT) images using a clustering algorithm called Floor of Log (FoL), which proves to be efficient, besides having good application times, and has the potential to be a medical diagnostic aid tool.

TL;DR: In this article, a photomicrographic analysis was performed on non-grain oriented electrical steels with 1.28% silicon, cold-rolled with reductions of 50% and 70%, annealed in box at 730°C for 12h, and subjected to a subsequent annealing heat treatment for grain growth at 620°C, 730 Â c, 840 Â C and 900 ÂC for 1, 10, 100 and 1000 Â min at each temperature.

TL;DR: The proposed Cascaded Volumetric Fully Convolutional Networks enables the visualization and iteration of the segmented volume in 3D so that the doctor can analyze the entire structure of the heart along with the circulatory network.

TL;DR: The highlights of the proposed SPPW method of segmentation are its short average segmentation time per image, and its metrics values, which are often higher than the ones obtained by other methods.