Método Fuzzy para auxílio ao diagnóstico de câncer de mama em ambiente inteligente de telediagnóstico colaborativo para apoio à tomada de decisão

Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA.

Desenvolvimento formal de aplicações para smartcards

Smart card applications represent a growing market. Usually this kind of application manipulate and store critical information that requires some level of security, such as financial or confidential information. The quality and trustworthiness of smart card software can be improved through a rigorous development process that embraces formal techniques of software engineering. In this work we propose the BSmart method, a specialization of the B formal method dedicated to the development of smart card Java Card applications. The method describes how a Java Card application can be generated from a B refinement process of its formal abstract specification. The development is supported by a set of tools, which automates the generation of some required refinements and the translation to Java Card client (host) and server (applet) applications. With respect to verification, the method development process was formalized and verified in the B method, using the Atelier B tool [Cle12a]. We emphasize that the Java Card application is translated from the last stage of refinement, named implementation. This translation process was specified in ASF+SDF [BKV08], describing the grammar of both languages (SDF) and the code transformations through rewrite rules (ASF). This specification was an important support during the translator development and contributes to the tool documentation. We also emphasize the KitSmart library [Dut06, San12], an essential component of BSmart, containing models of all 93 classes/interfaces of Java Card API 2:2:2, of Java/Java Card data types and machines that can be useful for the specifier, but are not part of the standard Java Card library. In other to validate the method, its tool support and the KitSmart, we developed an electronic passport application following the BSmart method. We believe that the results reached in this work contribute to Java Card development, allowing the generation of complete (client and server components), and less subject to errors, Java Card applications.