Breast cancer accounts for the highest number of female deaths worldwide. Early detection of the disease is essential to increase the chances of treatment and cure of patients. Infrared thermography has emerged as a promising technique for diagnosis of the disease due to its low cost and that it does not emit harmful radiation, and it gives good results when applied in young women. This work uses convolutional neural networks in a database of 440 infrared images of 88 patients, classifying them into two classes: normal and pathology. During the training of the networks, we use transfer learning of the following convolutional neural network architectures: AlexNet, GoogLeNet, ResNet-18, VGG-16, and VGG-19. Our results show the great potential of using deep learning techniques combined with infrared images in the aid of breast cancer diagnosis.

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Breast cancer kills a large number of women around the world. Infrared thermography is a promising screening technique which does not involve harmful radiation for the patient and has a relatively low cost. This work proposes an approach for classifying patients into three different classes using infrared images: healthy patients, patients with benign changes and patients with cancer (malignant changes). A set of features is extracted from each image and two approaches are used in the classification process. The first is based on Artificial Neural Networks while the second is based on Support Vector Machines. The proposed approach shows a great potential to be used as a screening diagnosis technique for early breast cancer detection.

Objectives: Temporal Enhanced Ultrasound (TeUS) is a new ultrasound-based imaging technique that provides tissue-specific information. Recent studies have shown the potential of TeUS for improving tissue characterization in prostate cancer diagnosis. We study the temporal properties of TeUS – temporal order and length – and present a new framework to assess their impact on tissue information. Methods: We utilize a probabilistic modeling approach using Hidden Markov Models (HMMs) to capture the temporal signatures of malignant and benign tissues from TeUS signals of 9 patients. We model signals of benign and malignant tissues (284 and 286 signals, respectively) in their original temporal order as well as under order permutations. We then compare the resulting models using the Kullback-Liebler divergence and assess their performance differences in characterization. Moreover, we train HMMs using TeUS signals of different durations and compare their model performance when differentiating tissue types. Results: Our findings demonstrate that models of order-preserved signals perform statistically significantly better (85% accuracy) in tissue characterization compared to models of order-altered signals (62% accuracy). The performance degrades as more changes in signal-order are introduced. Additionally, models trained on shorter sequences perform as accurately as models of longer sequences. Conclusion: The work presented here strongly indicates that temporal order has substantial impact on TeUS performance, thus it plays a significant role in conveying tissue-specific information. Further-more, shorter TeUS signals can relay sufficient information to accurately distinguish between tissue types. Significance: Under-standing the impact of TeUS properties facilitates the process of its adopting in diagnostic procedures and provides insights on improving its acquisition.

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Esta pesquisa teve como objetivo conhecer o nível de percepção dos alunos do Ensino Fundamental em escolas no cariri Paraibano, sobre a temática agroecologia e educação ambiental, permitindo que estes desenvolvam atividades sustentáveis no uso dos recursos naturais. A pesquisa foi executada na escola Estadual Francisco de Assis Gonzaga, localizado no município de Prata-PB, e na escola Agrotécnica Dep. Evaldo Gonçalves de Queiroz, localizado no município de Sumé-PB. Trata-se de um trabalho descritivo e exploratório, cujo foco centra-se em conhecer a percepção dos alunos de ensino fundamental, sobre as questões que envolvem agroecologia e educação ambiental. O público respondente da pesquisa foi composto por alunos do turno diurno das turmas do 8º e 9º ano, da idade de 13 a 19, totalizando 46 alunos, no município da Prata-PB e alunos do 7° ano com faixa etária entre 12 e 14 anos totalizando 32 alunos, no município de Sumé-PB. Os resultados obtidos demonstraram que a maioria dos alunos da E.E.F. Dep. Evaldo Gonçalves de Queiroz, localizado no município de Sumé-PB, apresentavam um maior conhecimento da temática agroecologia e das questões ambientais, como também sabiam diferenciar os questionamentos no tocante a análise entre práticas agroecológicas e práticas convencionais, comparados aos alunos da Escola Estadual Francisco de Assis Gonzaga, localizado no município de Prata-PB, o que pode está relacionado, que os alunos do município de Sumé tem uma maior vivência do campo, dentro do ambiente escolar, tendo em vista que os alunos moram na zona rural. Assim, proporcionar espaços de intervivência permite que novos conceitos sejam gerados, possibilitando transformações saudáveis na escola e na população.

Recent studies have shown the value of Temporal Enhanced Ultrasound (TeUS) imaging for tissue characterization in transrectal ultrasound-guided prostate biopsies. Here, we present results of experiments designed to study the impact of temporal order of the data in TeUS signals. We assess the impact of variations in temporal order on the ability to automatically distinguish benign prostate-tissue from malignant tissue. We have previously used Hidden Markov Models (HMMs) to model TeUS data, as HMMs capture temporal order in time series. In the work presented here, we use HMMs to model malignant and benign tissues; the models are trained and tested on TeUS signals while introducing variation to their temporal order. We first model the signals in their original temporal order, followed by modeling the same signals under various time rearrangements. We compare the performance of these models for tissue characterization. Our results show that models trained over the original order-preserving signals perform statistically significantly better for distinguishing between malignant and benign tissues, than those trained on rearranged signals. The performance degrades as the amount of temporal-variation increases. Specifically, accuracy of tissue characterization decreases from 85% using models trained on original signals to 62% using models trained and tested on signals that are completely temporally-rearranged. These results indicate the importance of order in characterization of tissue malignancy from TeUS data.

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