The use of computational techniques in the processing of histopathological images allows the study of the structural organization of tissues and their changes through diseases. This study aims to develop a tool for classifying histopathological images from breast lesions in the benign and malignant classes through magnification scales by an innovative way of using transfer learning techniques combined with machine learning methods and deep learning. The BreakHis dataset was used in the experiments, consisting of histopathological images of breast cancer with different tumor enlargement scales classified as Malignant or Benign. In this study, various combinations of Extractor-Classifiers were performed, thus seeking to compare the best model. Among the results achieved, the best Extractor-Classifier set formed was CNN DenseNet201, acting as an extractor, with the SVM RBF classifier, obtaining accuracy of 95.39% and precision of 95.43% for the 200X magnification factor. Different models were generated, compared to each other, and validated based on methods in the literature to validate the experiments, thus showing the effectiveness of the proposed model. The proposed method obtained satisfactory results, reaching results in the state-of-the-art for the multi-classification of subclasses from the different scale factors found in the BreakHis dataset and obtaining better results in the classification time.
Técnicas de manutenção preditiva permitem que os equipamentos sejam monitorados sem a necessidade de interromper sua operação, reduzindo a frequência de manutenção corretiva. Este trabalho propõe um sistema não-invasivo de detecção de vibração para analisar o estado de uma máquina elétrica trifásica usada na bomba centrífuga usando um sensor de sistema micro eletromecânico (MEMS) composto por acelerômetro e giroscópio de múltiplos eixos. A análise estrutural forneceu um meio de extrair as características do sinal no domínio do tempo. A classificação foi realizada para analisar os atributos do sinal de diferentes combinações em três eixos. Os resultados mostram que o uso dos sinais de aceleradores e giroscópios melhora o desempenho dos classificadores, como é o caso do Linear SVM, que atingiu 100% para todas as condições de monitoramento. Além disso, a extração dos atributos do sinal usando o método de análise estrutural do SCM é rápida, permitindo que seja usado em sistemas embarcados.
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