2 resultados para computer assisted diagnosis

em Universidade Federal do Rio Grande do Norte(UFRN)


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The use of technology tools for teaching and learning has grown increasingly in our daily life. In this context, a branch that has had tremendous growth is the area of teaching and learning language through computational tools. The study of CALL (Computer Assisted Language Learning), accomplished in this research, aims to evaluate existing tools in this context, focused specifically on the Japanese language; and from this study, accomplish the development of a new computational tool that can assist teaching/learning of the Japanese language. As results, we present a wide survey on the subject in various technologies/devices, as well as the complete development process of a new tool, the Karuchā Ships Invaders game, that proposes to teach basic concepts of the language, blended with entertainment, and still, focusing on the Brazilian students of Japanese language audience. We will present all the concept phases of the game and its evolution through the research, as well as an interface evaluation. Still, we present proposal and validation of a method to evaluate motivational aspects of computational tools with educational focus, and results extracted from an experiment accomplished with prospective users

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Lung cancer is one of the most common types of cancer and has the highest mortality rate. Patient survival is highly correlated with early detection. Computed Tomography technology services the early detection of lung cancer tremendously by offering aminimally invasive medical diagnostic tool. However, the large amount of data per examination makes the interpretation difficult. This leads to omission of nodules by human radiologist. This thesis presents a development of a computer-aided diagnosis system (CADe) tool for the detection of lung nodules in Computed Tomography study. The system, called LCD-OpenPACS (Lung Cancer Detection - OpenPACS) should be integrated into the OpenPACS system and have all the requirements for use in the workflow of health facilities belonging to the SUS (Brazilian health system). The LCD-OpenPACS made use of image processing techniques (Region Growing and Watershed), feature extraction (Histogram of Gradient Oriented), dimensionality reduction (Principal Component Analysis) and classifier (Support Vector Machine). System was tested on 220 cases, totaling 296 pulmonary nodules, with sensitivity of 94.4% and 7.04 false positives per case. The total time for processing was approximately 10 minutes per case. The system has detected pulmonary nodules (solitary, juxtavascular, ground-glass opacity and juxtapleural) between 3 mm and 30 mm.