Classificação de nódulos pulmonares em imagens tomográficas utilizando redes neurais artificiais em cascata

Lung cancer is the most common of malignant tumors, with 1.59 million new cases worldwide in 2012. Early detection is the main factor to determine the survival of patients affected by this disease. Furthermore, the correct classification is important to define the most appropriate therapeutic approach as well as suggest the prognosis and the clinical disease evolution. Among the exams used to detect lung cancer, computed tomography have been the most indicated. However, CT images are naturally complex and even experts medical are subject to fault detection or classification. In order to assist the detection of malignant tumors, computer-aided diagnosis systems have been developed to aid reduce the amount of false positives biopsies. In this work it was developed an automatic classification system of pulmonary nodules on CT images by using Artificial Neural Networks. Morphological, texture and intensity attributes were extracted from lung nodules cut tomographic images using elliptical regions of interest that they were subsequently segmented by Otsu method. These features were selected through statistical tests that compare populations (T test of Student and U test of Mann-Whitney); from which it originated a ranking. The features after selected, were inserted in Artificial Neural Networks (backpropagation) to compose two types of classification; one to classify nodules in malignant and benign (network 1); and another to classify two types of malignancies (network 2); featuring a cascade classifier. The best networks were associated and its performance was measured by the area under the ROC curve, where the network 1 and network 2 achieved performance equal to 0.901 and 0.892 respectively.

Extensões na arquitetura SDN para o provisionamento de QoS através do monitoramento e uso de múltiplos caminhos

The substantial increase in the number of applications offered through the computer networks, as well as in the volume of traffic forwarded through the network, have hampered to assure adequate service level to users. The Quality of Service (QoS) offer, honoring specified parameters in Service Level Agreements (SLA), established between the service providers and their clients, composes a traditional and extensive computer networks’ research area. Several schemes proposals for the provision of QoS were presented in the last three decades, but the acting scope of these proposals is always limited due to some factors, including the limited development of the network hardware and software, generally belonging to a single manufacturer. The advent of Software Defined Networking (SDN), along with the maturation of its main materialization, the OpenFlow protocol, allowed the decoupling between network hardware and software, through an architecture which provides a control plane and a data plane. This eases the computer networks scenario, allowing that new abstractions are applied in the hardware composing the data plane, through the development of new software pieces which are executed in the control plane. This dissertation investigates the QoS offer through the use and extension of the SDN architecture. Based on the proposal of two new modules, one to perform the data plane monitoring, SDNMon, and the second, MP-ROUTING, developed to determine the use of multiple paths in the forwarding of data referring to a flow, we demonstrated in this work that some QoS metrics specified in the SLAs, such as bandwidth, can be honored. Both modules were implemented and evaluated through a prototype. The evaluation results referring to several aspects of both proposed modules are presented in this dissertation, showing the obtained accuracy of the monitoring module SDNMon and the QoS gains due to the utilization of multiple paths defined by the MP-Routing, when forwarding data flow through the SDN.

Múltiplos fluxos ópticos e o processamento atentivo da informação visual

The environment in which we live in, we constantly deal with a huge amount of dynamic information, thus, attention is an indispensable cognitive resource that allows an effective selection of stimuli for our survival. From this, investigating how we process our encouragement in movements and how the attention spreads into a space to serve more than one stimuli simultanously is something very important. The behavioural urgence hipothesis suggests that the encouragement in a movement of approaching shows a certain priority in the process related to objects which are in a movement away, but there are researches that point out that it might not happen in an attentive phase, but instead as a priorization of motor response. There are also many controversies found in researches about attentive focalization, in which some studies suggest that the focus of attention would work in a similar manner to a zoom lens, while some searches indicate that the focus of attention could be shared to answer some stimuli in non contiguous regions. This study tried to investigate through two experiments the effect of attentive priorization by encouragement in movements and how the attention is spread with distractors stimuli. The first experiment investigated if the amount of moving flows really influenced in the process of information. The results indicate an effect of priorization of the flows guided in relation to aleatory ones and also from the unique flow due to dual flow. The second experiment investigated how the distribution of attention is in a space with the use of flows as an exogenous cue. The results indicate that the focus of attention works as the one suggested in the zoom lens model.