One of the most globally common types of cancer is skin cancer, where melanoma is the most deadly form. An important and promising tool for diagnosing diseases such as skin cancer is computer aided diagnostics, a tool which utilizes machine learning to predict and classify cancer. Limiting the complexity of the data, known as feature selection, can potentially improve classification accuracy. This report evaluates the accuracy of four different classifiers - Support Vector Machine, Naive Bayes, Decision Tree and Artificial Neural Network - with four different feature selection methods - Sequantial Forward Selection, Sequantial Backward Selection, Entropy and Principal Component Analysis - on the PH2 skin cancer dataset, containing dermoscopic images of skin lesions and their respective metadata. The findings reveal that all feature selection methods led to an improved accuracy rate on at least one classifier compared to not using feature selection. Furthermore, certain feature selection methods resulted in a significant gain in accuracy, indicating the potential value of feature selection techniques in improving the accuracy and efficiency of machine learning classifiers in computer-aided diagnosis systems for melanoma skin cancer detection. However, the results also underscore the importance of careful selection of the number of features to avoid adverse effects on model performance. This research contributes to the field by demonstrating the impact of feature selection methods on melanoma skin cancer detection and highlighting considerations for their application. / En av de globalt vanligaste typerna av cancer är hudcancer, där melanom är den mest dödliga typen. Ett viktigt och effektivt verktyg för att diagnostisera sjukdomar som hudcancer är datorstödd diagnostik, ett verktyg som använder maskininlärning för att förutse och klassificera cancer. Att begränsa komplexiteten i data, känt som attributurval, kan potentiellt förbättra klassificeringsnoggrannheten. Denna rapport utvärderar noggrannheten hos fyra olika klassificerare - ”Support Vector Machine”, ”Naive Bayes”, ”Decision Tree” och ”Artificial Neural Network” - med fyra olika attributurvalsmetoder - ”Sequantial Forward Selection”, ”Sequantial Backward Selection”, ”Entropy” and ”Principal Component Analysis” - på PH2 hudcancerdatasetet, som innehåller dermoskopiska bilder av hudlesioner och deras respektive metadata. Resultaten visar att alla attributurvalsmetoder ledde till en förbättrad noggrannhetsgrad på minst en klassificerare jämfört med att inte använda attributurval. Dessutom resulterade vissa attributurvalsmetoder i en betydande ökning i noggrannhet, vilket indikerar det potentiella värdet av attributurvalstekniker för att förbättra noggrannheten och effektiviteten hos maskininlärningsklassificerare i datorstödda diagnossystem för detektering av melanom hudcancer. Däremot understryker resultaten också vikten av noggrant urval av antalet attribut för att undvika negativa effekter på modellens prestanda. Denna forskning bidrar till fältet genom att demonstrera inverkan av attributurvalsmetoder på detektering av melanom hudcancer och belysa överväganden för deras tillämpning.

Alzheimer’s disease is a neurodegenerative disease that affects approximately 6% of the global population aged over 65 and is forecasted to become even more prevalent in the future. Accurately diagnosing the disease in an early stage can play a large role in improving the quality of life for the patient. One key development for performing this diagnosis is applying machine learning to perform computer-aided diagnosis. Current research in the field has been focused on removing assumptions about the used data sets, but in doing so they have often discarded objective metadata such as the patient’s age, sex or priormedical history. This study aimed to investigate the effect of including such metadata as additional input features to neural networks used for diagnosing Alzheimer’s disease through binary classification of magnetic resonance imaging scans. Two similar neural networks were developed and compared, one with these additional features and the other without them. Including the metadata led to significant improvements in the network’s classification accuracy, and should therefore be considered in future computer-aided diagnostic systems for Alzheimer’s disease. / Alzheimers sjukdom är en form av demens som påverkar ungefär 6% av den globala befolkningen som är äldre än 65 och förutspås bli ännu vanligare i framtiden. Tidig diagnos av sjukdomen är viktigt för att säkerställa högre livskvalitet för patienten. En viktig utveckling inom fältet är datorstödd diagnos av sjukdomen med hjälp av maskininlärning. Dagens forskning fokuserar på att ta bort subjektiva antaganden om datamängden som används, men har ofta även förkastat objektiv metadata såsom patientens ålder, kön eller tidigare medicinska historia. Denna studier ämnade därför undersöka om inkluderandet av denna metadata ledde till bättre prestanda hos neuronnät som används för datorstödd diagnos av Alzheimers genom binär klassificering av bilder tagna med magnetisk resonanstomografi. Två snarlika neuronnät utvecklades och jämfördes, med skillnaden att den ena även tog metadata om patienten som indata. Inkluderandet av metadatan ledde till en markant ökning i neuronnätets prestanda, och bör därför övervägas i framtida system för datorstödd diagnos av Alzheimers sjukdom.

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A análise de imagens médicas auxiliada por computador permite a análise quantitativa das anormalidades e garante maior precisão diagnóstica. Esse tipo de análise é importante para medicina nuclear com Single Photon Emission Computed Tomography (SPECT), pois no grupo de dados tridimensionais de imagens, padrões sutis de anormalidades muitas vezes são importantes achados clínicos. Porém, as imagens podem sofrer interferência de artefatos de atenuação da emissão de fótons por partes moles corporais, o que reduz sua acurácia diagnóstica. Desde que se possuam parâmetros de atenuação computados em um modelo que permita a comparação com imagens de um dado paciente, a interferência dos artefatos pode ser corrigida com ganho na acurácia diagnóstica, sem a necessidade de utilização de técnicas de correção que aumentem a dose de exposição à radiação pelo paciente. A proposta desse estudo foi a criação de um atlas de cintilografia de perfusão miocárdica, que foi obtido a partir de imagens de indíviduos normais, e o desenvolvimento de um algoritmo computacional para a detecção de anormalidades perfusionais miocárdicas, através da comparação estatística dos modelos do atlas com imagens de pacientes. Métodos de corregistro de imagens de mesma modalidade e outras técnicas de processamento de imagens foram estudados e utilizados para a comparação das imagens dos pacientes com o modelo apropriado. Pela análise visual dos modelos, verificou-se a sua validade como imagem representativa de normalidade perfusional. Para avaliação da detecção, a situação dos segmentos miocárdicos (normal ou anormal) indicada pelo algoritmo de detecção foi comparada com a situação apontada no laudo obtido pela concordância de dois especialistas, de modo a se verificar as concordâncias e discordâncias da técnica em relação ao laudo e se obter a significância estatística. Com isso, verificou-se um índice de concordância positiva da técnica em relação ao laudo de aproximadamente 50%, de concordância negativa próxima a 82% e de concordância geral próxima a 68%. O teste exato de Fisher foi aplicado às tabelas de contingência, obtendo-se um valor de p bicaudal inferior a 0,0001, indicando uma probabilidade muito baixa de as concordâncias terem sido obtidas pelo acaso. Melhorias no algoritmo deverão ser implementadas e testes futuros com um padrão-ouro efetivo serão realizados para validação da técnica. / The computer-aided medical imaging analysis allows the quantitative analysis of abnormalities and enhances diagnostic accuracy. This type of analysis is important for nuclear medicine that uses Single Photon Emission Computed Tomography (SPECT), because in the group of three-dimensional data images, subtle patterns of abnormalities often are important clinical findings. However, images can suffer interference from attenuation artifacts of the emission of photons by soft parts of the body, which reduces their diagnostic accuracy. Since there are attenuation parameters computed in a template that allows for comparison with images of a given patient, the artifacts interference can be corrected with a gain in diagnostic accuracy, without the need of using correction techniques that increase the radiation exposure dose of the patient. The purpose of this study was to create an atlas of myocardial perfusion scintigraphy, which was obtained from images of normal individuals and the development of a computational algorithm for detection of myocardial perfusion abnormalities by statistical comparison of atlas templates with images of patients. Methods of image registration of same modality and other image processing techniques were studied and used for comparison of patient images with the appropriate template. By the visual analysis of the templates it was found its validity as a representative image of normal perfusion. For the detection evaluation, the situation of myocardial segments (normal or abnormal) indicated by the detection algorithm was compared with the situation indicated in the medical appraisal report obtained by agreement of two specialists in order to determine the agreement and disagreement of the technique regarding the medical appraisal report and obtaining the statistical significance. Thus, there was a positive agreement index of the technique regarding the medical appraisal report of approximately 50%, a negative agreement index close to 82% and a general agreement index near 68%. The Fisher exact test was applied to the contingency tables, yielding a two-sided p-value less than 0.0001, that indicates a very low probability of the agreements have been obtained by chance. Algorithm improvements should be implemented and further tests with an effective gold-standard will be conducted to validate the technique.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-08-14T18:15:08Z No. of bitstreams: 1 DanielCosta.pdf: 1087754 bytes, checksum: ada5f863f42efd8298fff788c37bded3 (MD5) / Made available in DSpace on 2017-08-14T18:15:08Z (GMT). No. of bitstreams: 1 DanielCosta.pdf: 1087754 bytes, checksum: ada5f863f42efd8298fff788c37bded3 (MD5) Previous issue date: 2008-02-25 / Female breast cancer is the major cause of death in western countries. Efforts in Computer Vision have been made in order to add improve the diagnostic accuracy by radiologists. In this work, we present a methodology that uses independent component analysis (ICA) along with support vector machine (SVM) and linear discriminant analysis (LDA) to distinguish between mass or non-mass and benign or malign tissues from mammograms. As a result, it was found that: LDA reaches 90,11% of accuracy to discriminante between mass or non-mass and 95,38% to discriminate between benign or malignant tissues in DDSM database and in mini-MIAS database we obtained 85% to discriminate between mass or non-mass and 92% of accuracy to discriminate between benign or malignant tissues; SVM reaches 99,55% of accuracy to discriminate between mass or non-mass and the same percentage to discriminate between benign or malignat tissues in DDSM database whereas, and in MIAS database it was obtained 98% to discriminate between mass or non-mass and 100% to discriminate between benign or malignant tissues. / Câncer de mama feminino é o câncer que mais causa morte nos países ocidentais. Esforços em processamento de imagens foram feitos para melhorar a precisão dos diagnósticos por radiologistas. Neste trabalho, nós apresentamos uma metodologia que usa análise de componentes independentes (ICA) junto com análise discriminante linear (LDA) e máquina de vetor de suporte (SVM) para distinguir as imagens entre nódulos ou não-nódulos e os tecidos em benignos ou malignos. Como resultado, obteve-se com LDA 90,11% de acurácia na discriminação entre nódulo ou não-nódulo e 95,38% na discriminação de tecidos benignos ou malignos na base de dados DDSM. Na base de dados mini- MIAS, obteve-se 85% e 92% na discriminação entre nódulos ou não-nódulos e tecidos benignos ou malignos respectivamente. Com SVM, alcançou-se uma taxa de até 99,55% na discriminação de nódulos ou não-nódulos e a mesma porcentagem na discriminação entre tecidos benignos ou malignos na base de dados DDSM enquanto que na base de dados mini-MIAS, obteve-se 98% e até 100% na discriminação de nódulos ou não-nódulos e tecidos benignos ou malignos, respectivamente.

Σκοπός αυτής της διπλωματικής εργασίας είναι η εξαγωγή γνώσης και χρήσιμων συμπερασμάτων για το σχετικά αδιερεύνητο θέμα της διάγνωσης των μαθησιακών δυσκολιών. Χρησιμοποιήθηκαν δεδομένα καταγραφής ηλεκτροεγκεφαλογραφημάτων ηρεμίας και εγκεφαλικών προκλητών δυναμικών υγιών και ατόμων με μαθησιακές δυσκολίες, τα οποία συλλέχθηκαν στο εργαστήριο Ψυχοφυσιολογίας του Αιγινητείου Νοσοκομείου. Από την ανάλυση αυτών των σημάτων προσδιορίστηκαν παράμετροι (π.χ συγκεκριμένες κορυφώσεις) που διαφοροποιούν στατιστικά τα άτομα με μαθησιακές δυσκολίες σε σχέση με τους υγιείς. Παράλληλα, εξετάστηκαν παράμετροι από την κλασική θεωρία βιοσημάτων όπως η ενέργεια και οι χαρακτηριστικοί ρυθμοί. Τέλος, επιλύθηκε το αντίστροφο πρόβλημα της ηλεκτροεγκεφαλογραφίας ώστε να βρεθούν οι ρευματικές πηγές που προκαλούν τα αντίστοιχα σήματα στην επιφάνεια του κεφαλιού. Από τις πηγές αυτές επιδιώχθηκε ο προσδιορισμός περιοχών του εγκεφάλου που πιθανώς να είναι υπεύθυνες για την εμφάνιση μαθησιακών δυσκολιών. / The present thesis aims at the extraction of knowledge and useful conclusions for the relatively uninvestigated phenomenon of learning difficulties. Patients and healthy controls were evaluated by a computerized version of the digit span Wechsler test and EEG/ERP signals were recorded from 15 scalp electrodes based on the international 10-20 system of electroencephalography. The phenomenon was investigated via processing and analysis of EEG/ERP signals of healthy and persons with learning difficulties. Some features were extracted from these signals that statistically differentiate these two groups. Furthermore, features from classical theory of biosignals such as energy and characteristic rhythms were investigated. Finally, the so-called electroencephalography inverse problem was solved in order to define the internal current sources. The localization of such sources in the brain aimed at defining brain regions that are potentially responsible for learning difficulties.

A análise de imagens médicas auxiliada por computador permite a análise quantitativa das anormalidades e garante maior precisão diagnóstica. Esse tipo de análise é importante para medicina nuclear com Single Photon Emission Computed Tomography (SPECT), pois no grupo de dados tridimensionais de imagens, padrões sutis de anormalidades muitas vezes são importantes achados clínicos. Porém, as imagens podem sofrer interferência de artefatos de atenuação da emissão de fótons por partes moles corporais, o que reduz sua acurácia diagnóstica. Desde que se possuam parâmetros de atenuação computados em um modelo que permita a comparação com imagens de um dado paciente, a interferência dos artefatos pode ser corrigida com ganho na acurácia diagnóstica, sem a necessidade de utilização de técnicas de correção que aumentem a dose de exposição à radiação pelo paciente. A proposta desse estudo foi a criação de um atlas de cintilografia de perfusão miocárdica, que foi obtido a partir de imagens de indíviduos normais, e o desenvolvimento de um algoritmo computacional para a detecção de anormalidades perfusionais miocárdicas, através da comparação estatística dos modelos do atlas com imagens de pacientes. Métodos de corregistro de imagens de mesma modalidade e outras técnicas de processamento de imagens foram estudados e utilizados para a comparação das imagens dos pacientes com o modelo apropriado. Pela análise visual dos modelos, verificou-se a sua validade como imagem representativa de normalidade perfusional. Para avaliação da detecção, a situação dos segmentos miocárdicos (normal ou anormal) indicada pelo algoritmo de detecção foi comparada com a situação apontada no laudo obtido pela concordância de dois especialistas, de modo a se verificar as concordâncias e discordâncias da técnica em relação ao laudo e se obter a significância estatística. Com isso, verificou-se um índice de concordância positiva da técnica em relação ao laudo de aproximadamente 50%, de concordância negativa próxima a 82% e de concordância geral próxima a 68%. O teste exato de Fisher foi aplicado às tabelas de contingência, obtendo-se um valor de p bicaudal inferior a 0,0001, indicando uma probabilidade muito baixa de as concordâncias terem sido obtidas pelo acaso. Melhorias no algoritmo deverão ser implementadas e testes futuros com um padrão-ouro efetivo serão realizados para validação da técnica. / The computer-aided medical imaging analysis allows the quantitative analysis of abnormalities and enhances diagnostic accuracy. This type of analysis is important for nuclear medicine that uses Single Photon Emission Computed Tomography (SPECT), because in the group of three-dimensional data images, subtle patterns of abnormalities often are important clinical findings. However, images can suffer interference from attenuation artifacts of the emission of photons by soft parts of the body, which reduces their diagnostic accuracy. Since there are attenuation parameters computed in a template that allows for comparison with images of a given patient, the artifacts interference can be corrected with a gain in diagnostic accuracy, without the need of using correction techniques that increase the radiation exposure dose of the patient. The purpose of this study was to create an atlas of myocardial perfusion scintigraphy, which was obtained from images of normal individuals and the development of a computational algorithm for detection of myocardial perfusion abnormalities by statistical comparison of atlas templates with images of patients. Methods of image registration of same modality and other image processing techniques were studied and used for comparison of patient images with the appropriate template. By the visual analysis of the templates it was found its validity as a representative image of normal perfusion. For the detection evaluation, the situation of myocardial segments (normal or abnormal) indicated by the detection algorithm was compared with the situation indicated in the medical appraisal report obtained by agreement of two specialists in order to determine the agreement and disagreement of the technique regarding the medical appraisal report and obtaining the statistical significance. Thus, there was a positive agreement index of the technique regarding the medical appraisal report of approximately 50%, a negative agreement index close to 82% and a general agreement index near 68%. The Fisher exact test was applied to the contingency tables, yielding a two-sided p-value less than 0.0001, that indicates a very low probability of the agreements have been obtained by chance. Algorithm improvements should be implemented and further tests with an effective gold-standard will be conducted to validate the technique.

Dans le cadre de cette thèse, nous nous sommes intéressés à l’étude de l’apport d’une aide assistée par ordinateur au diagnostic de certaines maladies dégénératives du cerveau, en explorant les images de tomographie par émission de positons, par des techniques de traitement d’image et d’analyse statistique.Nous nous sommes intéressés à la représentation corticale des 116 régions anatomiques, en associant à chacune d’elles un vecteur d’attribut issu du calcul des 4 premiers moments des intensités de voxels, et en y incluant par ailleurs l’entropie. Sur la base de l’aire de courbes ROC, nous avons établi qualitativement la pertinence de chacune des régions anatomiques, en fonction du nombre de paramètres du vecteur d’attribut qui lui était associé, pour séparer le groupe des sujets sains de celui des sujets atteints de la maladie d’Alzheimer. Dans notre étude nous avons proposé une nouvelle approche de sélection de régions les plus pertinentes, nommée "combination matrix", en se basant sur un système combinatoire. Chaque région est caractérisée par les différentes combinaisons de son vecteur d’attribut. L’introduction des régions les plus pertinentes(en terme de pouvoir de séparation des sujets) dans le classificateur supervisé SVM nous a permis d’obtenir, malgré la réduction de dimension opérée, un taux de classification meilleur que celui obtenu en utilisant l’ensemble des régions. / Our research focuses on presenting a novel computer-aided diagnosis technique for brain Positrons Emission Tomography (PET) images. It processes and analyzes quantitatively these images, in order to better characterize and extract meaningful information for medical diagnosis. Our contribution is to present a new method of classifying brain 18 FDG PET images. Brain images are first segmented into 116 Regions Of Interest (ROI) using an atlas. After computing some statistical features (mean, standarddeviation, skewness, kurtosis and entropy) on these regions’ histogram, we defined a Separation Power Factor (SPF) associated to each region. This factor quantifies the ability of each region to separate neurodegenerative diseases like Alzheimer disease from Healthy Control (HC) brain images. A novel region-based approach is developed to classify brain 18FDG-PET images. The motivation of this work is to identify the best regional features for separating HC from AD patients, in order to reduce the number of features required to achieve an acceptable classification result while reducing computational time required for the classification task.

Le mélanome est la forme la plus grave de cancer de la peau. Cette thèse a contribué au développement de deux approches différentes pour le diagnostic assisté par ordinateur du mélanome : approche analytique et approche comparative.L'approche analytique imite le comportement du dermatologue en détectant les caractéristiques de malignité sur la base de méthodes analytiques populaires dans une première étape, et en combinant ces caractéristiques dans une deuxième étape. Nous avons étudié l’impacte d’un système du diagnostic automatique utilisant des images dermoscopique de lésions cutanées pigmentées sur le diagnostic de dermatologues. L'approche comparative, appelé concept du Vilain Petit Canard (VPC), suppose que les naevus chez le même patient ont tendance à partager certaines caractéristiques morphologiques ainsi que les dermatologues identifient quelques groupes de similarité. VPC est le naevus qui ne rentre dans aucune de ces groupes, susceptibles d'être mélanome. / Melanoma is the most serious type of skin cancer. This thesis focused on the development of two different approaches for computer-aided diagnosis of melanoma: analytical approach and comparative approach. The analytical approach mimics the dermatologist’s behavior by first detecting malignancy features based on popular analytical methods, and in a second step, by combining these features. We investigated to what extent the melanoma diagnosis can be impacted by an automatic system using dermoscopic images of pigmented skin lesions. The comparative approach, called Ugly Duckling (UD) concept, assumes that nevi in the same patient tend to share some morphological features so that dermatologists identify a few similarity clusters. UD is the nevus that does not fit into any of those clusters, likely to be suspicious. The goal was to model the ability of dermatologists to build consistent clusters of pigmented skin lesions in patients.

L'analyse automatique de différences anatomiques en neuroimagerie a de nombreuses applications pour la compréhension et l'aide au diagnostic de pathologies neurologiques. Récemment, il y a eu un intérêt croissant pour les méthodes de classification telles que les machines à vecteurs supports pour dépasser les limites des méthodes univariées traditionnelles. Cette thèse a pour thème l'apprentissage automatique pour l'analyse de populations et la classification de patients en neuroimagerie. Nous avons tout d'abord comparé les performances de différentes stratégies de classification, dans le cadre de la maladie d'Alzheimer à partir d'images IRM anatomiques de 509 sujets de la base de données ADNI. Ces différentes stratégies prennent insuffisamment en compte la distribution spatiale des \textit{features}. C'est pourquoi nous proposons un cadre original de régularisation spatiale et anatomique des machines à vecteurs supports pour des données de neuroimagerie volumiques ou surfaciques, dans le formalisme de la régularisation laplacienne. Cette méthode a été appliquée à deux problématiques cliniques: la maladie d'Alzheimer et les accidents vasculaires cérébraux. L'évaluation montre que la méthode permet d'obtenir des résultats cohérents anatomiquement et donc plus facilement interprétables, tout en maintenant des taux de classification élevés. / Brain image analyses have widely relied on univariate voxel-wise methods. In such analyses, brain images are first spatially registered to a common stereotaxic space, and then mass univariate statistical tests are performed in each voxel to detect significant group differences. However, the sensitivity of theses approaches is limited when the differences involve a combination of different brain structures. Recently, there has been a growing interest in support vector machines methods to overcome the limits of these analyses.This thesis focuses on machine learning methods for population analysis and patient classification in neuroimaging. We first evaluated the performances of different classification strategies for the identification of patients with Alzheimer's disease based on T1-weighted MRI of 509 subjects from the ADNI database. However, these methods do not take full advantage of the spatial distribution of the features. As a consequence, the optimal margin hyperplane is often scattered and lacks spatial coherence, making its anatomical interpretation difficult. Therefore, we introduced a framework to spatially regularize support vector machines for brain image analysis based on Laplacian regularization operators. The proposed framework was then applied to the analysis of stroke and of Alzheimer's disease. The results demonstrated that the proposed classifier generates less-noisy and consequently more interpretable feature maps with no loss of classification performance.