Classification of human parasitic worm using microscopic image processing technique

Human parasitic infection causes diseases to people whether this infection will be inside the body called endoparasites, or outside of the body called ectoparasites. Human intestinal parasite worms infected by air, food, and water are the causes of major diseases and health problems. So in this study, a technique to identify two types of parasites in human fecal, that is, the eggs of the worms is proposed. In this strategy, digital image processing methods such as noise reduction, contrast enhancement, and other morphological process are applied to extract the eggs images based on their features. The technique suggested in this study enables us to classify two different parasite eggs from their microscopic images which are roundworms (Ascaris lumbricoides ova, ALO) and whipworms (Trichuris trichiura ova, TTO). This proposed recognition method includes three stages. The first stage is a pre-processing sub-system, which is used to obtain unique features after performing noise reduction, contrast enhancement, edge enhancement, and detection. The next stage is an extraction mechanism which is based on five features of the three characteristics (shape, shell smoothness, and size. The final stage, the Filtration with Determinations Thresholds System (F-DTS) classifier is used to recognize the process using the ranges of feature values as a database to identify and classify the two types of parasites. The overall success rates are 93% and 94% in Ascaris lumbricoides and Trichuris trichiura, respectively

How automated image analysis techniques help scientists in species identification and classification?

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre­ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef­forts on identification of species include specimens’ image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179–193

Aprendendo características de imagens por redes convolucionais sob restrição de dados supervisionados

Orientador: Alexandre Xavier FalcãoDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: A análise de imagens vem sendo largamente aplicada em diversas áreas das Ciências e Engenharia, com o intuito de extrair e interpretar o conteúdo de interesse em aplicações que variam de uma simple análise de códigos de barras ao diagnóstico automatizado de doenças. Entretanto, as soluções do Estado da Arte baseadas em redes neurais com múltiplas camadas usualmente requerem um elevado número de amostras anotadas (rotuladas), implicando em um considerável esforço humano na identificação, isolamento, e anotação dessas amostras em grandes bases de dados. O problema é agravado quando tal anotação requer especialistas no domínio da aplicação, tal como em Medicina e Agricultura, constituindo um inconveniente crucial em tais aplicações. Neste contexto, as Redes de Convolução (Convolution Networks - ConvNets), estão entre as abordagens mais bem sucedidas na extração de características de imagens, tal que, sua associação com Perceptrons Multi-Camadas (Multi Layer Perceptron - MLP) ou Máquinas de Vetores de Suporte (Support Vector Machines - SVM) permite uma classificação de amostras bastante efetiva. Outro problema importante de tais técnicas se encontra na alta dimensionalidade de suas características, que dificulta o processo de análise da distribuição das amostras por métodos baseados em distância Euclidiana, como agrupamento e visualização de dados multidimensionais. Considerando tais problemas, avaliamos as principais estratégias no projeto de ConvNets, a saber, Aprendizado de Arquitetura (Architecture Learning - AL), Aprendizado de Filtros (Filter Learning - FL) e Aprendizado por Transferência de Domínio (Transfer Learning - TL) em relação a sua capacidade de aprendizado num conjunto limitado de amostras anotadas. E, para confirmar a eficácia no aprendizado de características, analisamos a melhoria do classificador conforme o número de amostras aumenta durante o aprendizado ativo. Métodos de data augmentation também foram avaliados como uma potencial estratégia para lidar com a ausência de amostras anotadas. Finalmente, apresentamos os principais resultados do trabalho numa aplicação real ¿ o diagnóstico de parasitos intestinais ¿ em comparação com os descritores do Estado da Arte. Por fim, pudemos concluir que TL se apresenta como a melhor estratégia, sob restrição de dados supervisionados, sempre que tivermos uma rede previamente aprendida que se aplique ao problema em questão. Caso contrário, AL se apresenta como a segunda melhor alternativa. Pudemos ainda observar a eficácia da Análise Discriminante Linear (Linear Discriminant Analysis - LDA) em reduzir consideravelmente o espaço de características criado pelas ConvNets, permitindo uma melhor compreensão dos especialistas sobre os processos de aprendizado de características e aprendizado ativo, por meio de técnicas de visualização de dados multidimensionais. Estes importantes resultados sugerem que uma interação entre aprendizado de características, aprendizado ativo, e especialistas, pode beneficiar consideravelmente o aprendizado de máquinaAbstract: Image analysis has been widely employed in many areas of the Sciences and Engineering to extract and interpret high-level information from images, with applications ranging from a simple bar code analysis to the diagnosis of diseases. However, the state-of-the-art solutions based on deep learning often require a training set with a high number of annotated (labeled) examples. This may imply significant human effort in sample identification, isolation, and labeling from large image databases, specially when image annotation asks for specialists in the application domain, such as in Medicine and Agriculture, such requirement constitutes a crucial drawback. In this context, Convolution Networks (ConvNets) are among the most successful approaches for image feature extraction, such that their combination with a Multi-Layer Perceptron (MLP) network or a Support Vector Machine (SVM) can be used for effective sample classification. Another problem in these techniques is the resulting high-dimension feature space, which makes difficult the analysis of the sample distribution by the commonly used distance based data clustering and visualization methods. In this work, we analyze both problems by assessing the main strategies for ConvNet design, namely Architecture Learning (AL), Filter Learning (FL), and Transfer Learning (TL), according to their capability of learning from a limited number of labeled examples, and by evaluating the impact of feature space reduction techniques in distance-based data classification and visualization. In order to confirm the effectiveness of feature learning, we analyze the progress of the classifier as the number of supervised samples increase during active learning. Data augmentation has also been evaluated as a potential strategy to cope with the absence of labeled examples. Finally, we demonstrate the main results of the work for a real application ¿ the diagnosis of intestinal parasites ¿ in comparison to the state-of-the-art image descriptors. In conclusion, TL has shown to be the best strategy, under supervised data constraint, whenever we count with a learned network that suits the problem. When this is not the case, AL comes as the second best alternative. We have also observed the effectiveness of Linear Discriminant Analysis (LDA) in considerably reducing the feature space created by ConvNets to allow a better understanding of the feature learning and active learning processes by the expert through data visualization. This important result suggests an interplaying between feature and active learning with intervening of the experts to improve both processes as future workMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCNPQCAPE

An efficient approach to acoustic emission source identification based on harmonic wavelet packet and hierarchy support vector machine

A new approach for acoustic emission (AE) source type identification based on harmonic wavelet packet (HWPT) feature extraction and hierarchy support vector machine (H-SVM) classifier is proposed for solving the fatigue damage identification problem of helicopter moving component. In this approach, HWPT is employed to extract the energy feature of AE signals on different frequency bands, as well as to reduce the dimensionality of original data features. We trained the H-SVM classifier on a subset of the experimental data for known AE source type, and then tested on the remaining set of data. Also, the pressure off experiment on specimen of carbon fiber materials is investigated. The experimental results indicate that the proposed approach can implement AE source type identification effectively, and achieves better performance on computational efficiency and identification accuracy than wavelet packet (WPT) feature extraction and RBF neural network classification

Aprendizado ativo com aplicações ao diagnóstico de parasitos

Orientadores: Alexandre Xavier Falcão, Pedro Jussieu de RezendeTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Conjuntos de imagens têm crescido consideravelmente com o rápido avanço de inúmeras tecnologias de imagens, demandando soluções urgentes para o processamento, organização e recuperação da informação. O processamento, neste caso, objetiva anotar uma dada imagem atribuindo-na um rótulo que representa seu conteúdo semântico. A anotação é crucial para a organizaçao e recuperação efetiva da informação relacionada às imagens. No entanto, a anotação manual é inviável em grandes conjuntos de dados. Além disso, a anotação automática bem sucedida por um classificador de padrões depende fortemente da qualidade de um conjunto de treinamento reduzido. Técnicas de aprendizado ativo têm sido propostas para selecionar, a partir de um grande conjunto, amostras de treinamento representativas, com uma sugestão de rótulo que pode ser confirmado ou corrigido pelo especialista. Apesar disso, essas técnicas muitas vezes ignoram a necessidade de tempos de resposta interativos durante o processo de aprendizado ativo. Portanto, esta tese de doutorado apresenta métodos de aprendizado ativo que podem reduzir e/ou organizar um grande conjunto de dados, tal que a fase de seleção não requer reprocessá-lo inteiramente a cada iteração do aprendizado. Além disso, tal seleção pode ser interrompida quando o número de amostras desejadas, a partir do conjunto de dados reduzido e organizado, é identificado. Os métodos propostos mostram um progresso cada vez maior, primeiro apenas com a redução de dados, e em seguida com a subsequente organização do conjunto reduzido. Esta tese também aborda um problema real --- o diagnóstico de parasitos --- em que a existência de uma classe diversa (isto é, uma classe de impureza), com tamanho muito maior e amostras que são similares a alguns tipos de parasitos, torna a redução de dados consideravelmente menos eficaz. Este problema é finalmente contornado com um tipo de organização de dados diferente, que ainda permite tempos de resposta interativos e produz uma abordagem de aprendizado ativo melhor e robusta para o diagnóstico de parasitos. Os métodos desenvolvidos foram extensivamente avaliados com diferentes tipos de classificadores supervisionados e não-supervisionados utilizando conjunto de dados a partir de aplicações distintas e abordagens baselines que baseiam-se em seleção aleatória de amostras e/ou reprocessamento de todo o conjunto de dados a cada iteração do aprendizado. Por fim, esta tese demonstra que outras melhorias são obtidas com o aprendizado semi-supervisionadoAbstract: Image datasets have grown large with the fast advances and varieties of the imaging technologies, demanding urgent solutions for information processing, organization, and retrieval. Processing here aims to annotate the image by assigning to it a label that represents its semantic content. Annotation is crucial for the effective organization and retrieval of the information related to the images. However, manual annotation is unfeasible in large datasets and successful automatic annotation by a pattern classifier strongly depends on the quality of a much smaller training set. Active learning techniques have been proposed to select those representative training samples from the large dataset with a label suggestion, which can be either confirmed or corrected by the expert. Nevertheless, these techniques very often ignore the need for interactive response times during the active learning process. Therefore, this PhD thesis presents active learning methods that can reduce and/or organize the large dataset such that sample selection does not require to reprocess it entirely at every learning iteration. Moreover, it can be interrupted as soon as a desired number of samples from the reduced and organized dataset is identified. These methods show an increasing progress, first with data reduction only, and then with subsequent organization of the reduced dataset. However, the thesis also addresses a real problem --- the diagnosis of parasites --- in which the existence of a diverse class (i.e., the impurity class), with much larger size and samples that are similar to some types of parasites, makes data reduction considerably less effective. The problem is finally circumvented with a different type of data organization, which still allows interactive response times and yields a better and robust active learning approach for the diagnosis of parasites. The methods have been extensively assessed with different types of unsupervised and supervised classifiers using datasets from distinct applications and baseline approaches that rely on random sample selection and/or reprocess the entire dataset at each learning iteration. Finally, the thesis demonstrates that further improvements are obtained with semi-supervised learningDoutoradoCiência da ComputaçãoDoutora em Ciência da Computaçã

Image-based Skin Disease Detection and Classification through Bioinspired Machine Learning Approaches

A self-learning disease detection model will be useful for identifying skin infections in suspected individuals using skin images of infected patients. To detect skin diseases, some AI-based bioinspired models employ skin images. Skin infection is a common problem that is currently faced due to various reasons, such as food, water, environmental factors, and many others. Skin infections such as psoriasis, skin cancer, monkeypox, and tomato flu, among others, have a lower death rate but a significant impact on quality of life. Neural Networks (NNs) and Swarm intelligence (SI) based approaches are employed for skin disease diagnosis and classification through image processing. In this paper, the convolutional neural networks-based Cuckoo search algorithm (CNN-CS) is trained using the well-known multi-objective optimization technique cuckoo search. The performance of the suggested CNN-CS model is evaluated by comparing it with three commonly used metaheuristic-based classifiers: CNN-GA, CNN-BAT, and CNN-PSO. This comparison was based on various measures, including accuracy, precision, recall, and F1-score. These measures are calculated using the confusion matrices from the testing phase. The results of the experiments revealed that the proposed model has outperformed the others, achieving an accuracy of 97.72%

Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)

This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work

Klasifikasi Penurunan Kualitas Telur Ayam Ras Berdasarkan Warna Kerabang Menggunakan Support Vector Machine

Abstrak: Kualitas telur merupakan karakteristik dari telur yang menentukan kesegaran dan kesehatan telur. Salah satu unsur penentuan kualitas telur adalah berdasarkan kondisi kerabang telur. Warna kerabang yang berbeda dipengaruhi oleh jenis pigmen,konsentrasi pigmen dan struktur kerabang. Intensitas warna coklat dari kerabang telur dibedakan menjadi 3 kelompok yaitu telur dengan warna coklat tua, coklat dan coklat muda. Telur dengan warna kerabang coklat tua mempunyai kerabang lebih kuat dan tebal dibanding telur yang berwarna coklat muda. Oleh karena itu telur dengan warna kerabang coklat tua mengalami penurunan kualitas lebih rendah dibandingkan warna coklat dan coklat muda. Dalam penelitian ini dilakukan klasifikasi penurunan kualitas telur ayam ras berdasarkan warna kerabang menggunakan support vector machine. Semakin muda warna coklat kerabang telur semakin cepat penurunan kualitasnya. Dengan menggunakan SVM maka dapat diklasifikasikan kualitas telur berdasarkan warna.dengan  akurasi 80%. Kata Kunci: Intensitas, Klasifikasi, Kualitas,  Warna Kerabang Abstract: Egg quality is a characteristic of eggs that determines the freshness and health of eggs. One element of determining egg quality is based on the condition of the eggshell. Different shell colors are influenced by the type of pigment, pigment concentration and shell structure. The brown color intensity of eggshells is divided into 3 groups, namely eggs with dark brown, brown and light brown. Eggs with dark brown shells have stronger and thicker shells than light brown eggs. Therefore, eggs with dark brown color have a lower quality compared to brown and light brown. In this research,  a classification of the quality of chicken eggs based on eggshell was classified using a support vector machine. The younger the eggshell brown color the faster the quality decreases. By using SVM, egg quality can be classified based on color with 80% accuracy Keywords: Classification, Eggshell, Intensity, Qualit

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

A Malaria Diagnostic Tool Based on Computer Vision Screening and Visualization of Plasmodium falciparum Candidate Areas in Digitized Blood Smears

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