The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks

Detalhes bibliográficos
Autor(a) principal: Croon, Dennis Gerardus
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/103901
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networksPneumoniaSemantic Learning MachineSLMConvolutional Neural NetworksCNNMulti-Class Image PredictionsImage ClassificationGeometric SemanticGSGPGenetic ProgrammingDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsExtensive recent research has shown the importance of innovation in medical healthcare, with a focus on Pneumonia. It is vital and lifesaving to predict Pneumonia cases as fast as possible and preferably in advance of the symptoms. An online database source managed to gather Pneumonia-specific image data, with not just the presence of the infection, but also the nature of it, divided in bacterial- and viral infection. The first achievement is extracting valuable information from the X-Ray image datasets. Using several ImageNet pre-trained CNNs, knowledge can be gained from images and transferred to numeric arrays. This, both binary and multi-class classification data, requires a sophisticated prediction algorithm that recognizes X-Ray image patterns. Multiple, recently performed experiments show promising results about the innovative Semantic Learning Machine (SLM) that is essentially a geometric semantic hill climber for feedforward Neural Networks. This SLM is based on a derivation of the Geometric Semantic Genetic Programming (GSGP) mutation operator for real-value semantics. To prove the outperformance of the binary and multi-class SLM in general, a selection of commonly used algorithms is necessary in this research. A comprehensive hyperparameter optimization is performed for commonly used algorithms for those kinds of real-life problems, such as: Random Forest, Support Vector Machine, KNearestNeighbors and Neural Networks. The results of the SLM are promising for the Pneumonia application but could be used for all types of predictions based on images in combination with the CNN feature extractions.Uma extensa pesquisa recente mostrou a importância da inovação na assistência médica, com foco na pneumonia. É vital e salva-vidas prever os casos de pneumonia o mais rápido possível e, de preferência, antes dos sintomas. Uma fonte on-line conseguiu coletar dados de imagem específicos da pneumonia, identificando não apenas a presença da infecção, mas também seu tipo, bacteriana ou viral. A primeira conquista é extrair informações valiosas dos conjuntos de dados de imagem de raios-X. Usando várias CNNs pré-treinadas da ImageNet, é possível obter conhecimento das imagens e transferi-las para matrizes numéricas. Esses dados de classificação binários e multi-classe requerem um sofisticado algoritmo de predição que reconhece os padrões de imagem de raios-X. Vários experimentos realizados recentemente mostram resultados promissores sobre a inovadora Semantic Learning Machine (SLM), que é essencialmente um hill climber semântico geométrico para feedforward neural network. Esse SLM é baseado em uma derivação do operador de mutação da Geometric Semantic Genetic Programming (GSGP) para valor-reais semânticos. Para provar o desempenho superior do SLM binário e multi-classe em geral, é necessária uma seleção de algoritmos mais comuns na pesquisa. Uma otimização abrangente dos hiperparâmetros é realizada para algoritmos comumente utilizados para esses tipos de problemas na vida real, como Random Forest, Support Vector Machine,K-Nearest Neighbors and Neural Networks. Os resultados do SLM são promissores para o aplicativo pneumonia, mas podem ser usados para todos os tipos de previsões baseadas em imagens em combinação com as extrações de recursos da CNN.Castelli, MauroGonçalves, Ivo Carlos PereiraRUNCroon, Dennis Gerardus2020-09-11T14:37:51Z2020-07-292020-07-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/103901TID:202518396enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:49:32Zoai:run.unl.pt:10362/103901Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:05.849169Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks
title The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks
spellingShingle The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks
Croon, Dennis Gerardus
Pneumonia
Semantic Learning Machine
SLM
Convolutional Neural Networks
CNN
Multi-Class Image Predictions
Image Classification
Geometric Semantic
GSGP
Genetic Programming
title_short The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks
title_full The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks
title_fullStr The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks
title_full_unstemmed The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks
title_sort The outperformance of the semantic learning machine, against commonly used algorithms, for binary and multi-class medical image classification: combined with the usage of feature extraction by several convolutional neural networks
author Croon, Dennis Gerardus
author_facet Croon, Dennis Gerardus
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
Gonçalves, Ivo Carlos Pereira
RUN
dc.contributor.author.fl_str_mv Croon, Dennis Gerardus
dc.subject.por.fl_str_mv Pneumonia
Semantic Learning Machine
SLM
Convolutional Neural Networks
CNN
Multi-Class Image Predictions
Image Classification
Geometric Semantic
GSGP
Genetic Programming
topic Pneumonia
Semantic Learning Machine
SLM
Convolutional Neural Networks
CNN
Multi-Class Image Predictions
Image Classification
Geometric Semantic
GSGP
Genetic Programming
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2020
dc.date.none.fl_str_mv 2020-09-11T14:37:51Z
2020-07-29
2020-07-29T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/103901
TID:202518396
url http://hdl.handle.net/10362/103901
identifier_str_mv TID:202518396
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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