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Among all cancer‐related deaths, lung cancer leads all indicators, accounting for approximately 20% of all types. Patients diagnosed in the early stages have a 1‐year survival rate of 81% to 85%, while in an advanced stage have 15% to 19% chances of survival. The primary manifestation of this cancer is through pulmonary nodule on computed tomography images. In the early stages, it is a complex task even for experienced specialists and presents some challenges to classify these nodules in benign or malignant. So, to assist specialists, computer‐aided diagnosis systems have been used to improve the accuracy in the diagnosis. In this article, we explored and compared the use of random search, simulating annealing, and Tree‐of‐Parzen‐estimators algorithms of hyperparameter tuning to find the best architecture of a convolutional neural network to classify small pulmonary nodules in benign or malignant with a diameter of 5 to 10 mm. Our best model used result was the model using the simulating annealing algorithm and yielded an area under the receiver operating characteristic curve of 0.95, the sensitivity of 82%, the specificity of 94%, and accuracy of 88% using a balanced data set of nodules. Therefore, our model is capable of classifying early lung nodules, where the patients have bigger chances of survival.

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The Internet of things has features and operation differentiated from traditional networks, being a potential technology for usage in various areas of the educational context. Based on that technology, this article proposes to build a system computational established among heterogeneous objects to improve the apprentice learning process for Brazilian Sign Language and to promote social inclusion of brazilian deaf. Resumo.A Internet das Coisas possui características e funcionamento diferenciados das redes tradicionais, sendo uma potencial tecnologia para uso em diversas áreas do âmbito educacional. Baseado nessa tecnologia, este artigo propõe a construção de um sistema computacional estabelecido por objetos heterogêneos para aperfeiçoar o processo de ensino-aprendizagem da Língua Brasileira de Sinais e promover a inclusão social dos surdos brasileiros.

Lung cancer is a leading cause of death worldwide and its early detection is critical for patient survival. However, the diagnosis is still a challenging task, in which computeraided diagnosis (CADx) systems try to assist by providing a second opinion to a radiologist. In this work, we propose a 3D Convolutional Neural Network for classification of solid pulmonary nodules into benign and malignant. We evaluated different approaches for the nodule volume assembling and tuned our models in an automated fashion. Our models achieved satisfactory results, with AUC of 0.89, accuracy of 81.37% and a sensibility of 84.83%. Moreover, our results have shown that the first slices of a nodule provide the best results and only five nodule slices are enough for a 3D CNN achieve its best results.