Classification of Blood Cells Using Deep Learning Models

Human blood mainly comprises plasma, red blood cells, white blood cells, and platelets. The blood cells provide the body's cells oxygen to nourish them, shield them from infections, boost immunity, and aid in clotting. Human health is reflected in blood cells. The chances that a human being can be diagnosed with a disease are significantly influenced by their blood cell type and count. Therefore, blood cell classification is crucial because it helps identify diseases, including cancer, damaged bone marrow, benign tumors, and their growth. This classification allows hematologists to distinguish between different blood cell fragments so that the cause of diseases can be identified. Convolution neural networks are a deep learning technique that classifies images of human blood cells (RBCs, WBCs, and platelets) into their subtypes. For this study, transfer learning is used to apply different CNN pre-trained models, including VGG16, VGG19, ResNet-50, ResNet-101, ResNet-152, InceptionV3 MobileNetV2 and DenseNet-201 to the PBC dataset's normal DIB. The overall accuracy achieved with these models lies between 91.375-94.72%. A novel CNN-based framework has been presented to improve accuracy, and we were able to attain an accuracy of 99.91% on the PBC dataset

Object Detection in medical imaging

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision SystemsArtificial Intelligence, assisted by deep learning, has emerged in various fields of our society. These systems allow the automation and the improvement of several tasks, even surpassing, in some cases, human capability. Object detection methods are used nowadays in several areas, including medical imaging analysis. However, these methods are susceptible to errors, and there is a lack of a universally accepted method that can be applied across all types of applications with the needed precision in the medical field. Additionally, the application of object detectors in medical imaging analysis has yet to be thoroughly analyzed to achieve a richer understanding of the state of the art. To tackle these shortcomings, we present three studies with distinct goals. First, a quantitative and qualitative analysis of academic research was conducted to gather a perception of which object detectors are employed, the modality of medical imaging used, and the particular body parts under investigation. Secondly, we propose an optimized version of a widely used algorithm to overcome limitations commonly addressed in medical imaging by fine-tuning several hyperparameters. Thirdly, we develop a novel stacking approach to augment the precision of detections on medical imaging analysis. The findings show that despite the late arrival of object detection in medical imaging analysis, the number of publications has increased in recent years, demonstrating the significant potential for growth. Additionally, we establish that it is possible to address some constraints on the data through an exhaustive optimization of the algorithm. Finally, our last study highlights that there is still room for improvement in these advanced techniques, using, as an example, stacking approaches. The contributions of this dissertation are several, as it puts forward a deeper overview of the state-of-the-art applications of object detection algorithms in the medical field and presents strategies for addressing typical constraints in this area.A Inteligência Artificial, auxiliada pelo deep learning, tem emergido em diversas áreas da nossa sociedade. Estes sistemas permitem a automatização e a melhoria de diversas tarefas, superando mesmo, em alguns casos, a capacidade humana. Os métodos de detecção de objetos são utilizados atualmente em diversas áreas, inclusive na análise de imagens médicas. No entanto, esses métodos são suscetíveis a erros e falta um método universalmente aceite que possa ser aplicado em todos os tipos de aplicações com a precisão necessária na área médica. Além disso, a aplicação de detectores de objetos na análise de imagens médicas ainda precisa ser analisada minuciosamente para alcançar uma compreensão mais rica do estado da arte. Para enfrentar essas limitações, apresentamos três estudos com objetivos distintos. Inicialmente, uma análise quantitativa e qualitativa da pesquisa acadêmica foi realizada para obter uma percepção de quais detectores de objetos são empregues, a modalidade de imagem médica usada e as partes específicas do corpo sob investigação. Num segundo estudo, propomos uma versão otimizada de um algoritmo amplamente utilizado para superar limitações comumente abordadas em imagens médicas por meio do ajuste fino de vários hiperparâmetros. Em terceiro lugar, desenvolvemos uma nova abordagem de stacking para aumentar a precisão das detecções na análise de imagens médicas. Os resultados demostram que, apesar da chegada tardia da detecção de objetos na análise de imagens médicas, o número de publicações aumentou nos últimos anos, evidenciando o significativo potencial de crescimento. Adicionalmente, estabelecemos que é possível resolver algumas restrições nos dados por meio de uma otimização exaustiva do algoritmo. Finalmente, o nosso último estudo destaca que ainda há espaço para melhorias nessas técnicas avançadas, usando, como exemplo, abordagens de stacking. As contribuições desta dissertação são várias, apresentando uma visão geral em maior detalhe das aplicações de ponta dos algoritmos de detecção de objetos na área médica e apresenta estratégias para lidar com restrições típicas nesta área

Computer vision-based wood identification: a review

Wood identification is an important tool in many areas, from biology to cultural heritage. In the fight against illegal logging, it has a more necessary and impactful application. Identifying a wood sample to genus or species level is difficult, expensive and time-consuming, even when using the most recent methods, resulting in a growing need for a readily accessible and field-applicable method for scientific wood identification. Providing fast results and ease of use, computer vision-based technology is an economically accessible option currently applied to meet the demand for automated wood identification. However, despite the promising characteristics and accurate results of this method, it remains a niche research area in wood sciences and is little known in other fields of application such as cultural heritage. To share the results and applicability of computer vision-based wood identification, this paper reviews the most frequently cited and relevant published research based on computer vision and machine learning techniques, aiming to facilitate and promote the use of this technology in research and encourage its application among end-users who need quick and reliable results.info:eu-repo/semantics/publishedVersio

Deep Learning System for the Automatic Classification of Normal and Dysplastic Peripheral Blood Cells as a Support Tool for the Diagnosis

[eng] Clinical pathologists identify visually many morphological features to characterize the different normal cells, as well as the abnormal cell types whose presence in peripheral blood is the evidence of serious diseases. Disadvantages of visual morphological analysis are that it is time consuming, needs expertise to perform an objective review of the smears and is prone to inter-observer variability. Also, most of the morphological descriptions are given in qualitative terms and there is a lack of quantitative measures. The general objective of this thesis is the automatic recognition of normal and dysplastic cells circulating in blood in myelodysplastic syndromes using convolutional neural networks and digital image processing techniques. In order to accomplish this objective, this work starts with the design and development of a Mysql database to store information and images from patients and the development of a first classifier of four groups of cells, using convolutional neural networks as feature extractors. Then, a high- quality dataset of around 17,000 images of normal blood cells is compiled and used for the development of a recognition system of eight groups of blood cells. In this work, we compare two transfer learning approaches to find the best to classify the different cell types. In the second part of the thesis, a new convolutional neural network model for the diagnosis of myelodysplastic syndromes is developed. This model was validated by means of a proof of concept. It is considered among the first models that have been built for diagnosis support. The final work of the thesis is the integration of two convolutional networks in a modular system for the automatic classification of normal and abnormal cells. The methodology and models developed constitute a step forward to the implementation of a modular system to recognize automatically all cell types in a real setup in the laboratory.[spa] Los especialistas de laboratorio identifican visualmente muchas características morfológicas para identificar las diferentes células normales, así como los tipos de células anormales, cuya presencia en sangre periférica es evidencia de enfermedades graves. Algunas de las desventajas del análisis morfológico visual incluyen que toma mucho tiempo, necesita experiencia para realizar una revisión objetiva de los frotis y es propenso a la variabilidad entre observadores. Además, la mayoría de las descripciones morfológicas se proporcionan en términos cualitativos. Debido a lo expuesto anteriormente, es necesario establecer medidas cuantitativas. El objetivo general de esta tesis es el reconocimiento automático de células normales y células displásicas circulantes en sangre en síndromes mielodisplásicos mediante redes neuronales convolucionales y técnicas de procesamiento digital de imágenes. Para lograr este objetivo, este trabajo comenzó con el diseño y desarrollo de una base de datos Mysql para almacenar información e imágenes de pacientes y el desarrollo de un primer clasificador de cuatro grupos de células, utilizando redes neuronales convolucionales como extractores de características. Luego, se compila un conjunto de datos de alta calidad de alrededor de 17.000 imágenes de células sanguíneas normales y se utiliza para el desarrollo de un sistema de reconocimiento de ocho grupos de células sanguíneas. En este trabajo, comparamos dos enfoques de aprendizaje por transferencia para encontrar el mejor para clasificar los diferentes tipos de células. En la segunda parte de la tesis se desarrolla un nuevo modelo de red neuronal convolucional para el diagnóstico de síndromes mielodisplásicos. Este modelo fue validado mediante prueba de concepto. Se considera uno de los primeros modelos que se han construido para apoyar el diagnóstico. El trabajo final de la tesis es la integración de dos redes convolucionales en un sistema modular para la clasificación automática de células normales y anormales. La metodología y los modelos desarrollados constituyen un paso adelante hacia la implementación de un sistema modular para reconocer automáticamente todos los tipos de células en una configuración real en el laboratorio

Doctor of Philosophy

dissertationNeuroscientists are developing new imaging techniques and generating large volumes of data in an effort to understand the complex structure of the nervous system. The complexity and size of this data makes human interpretation a labor intensive task. To aid in the analysis, new segmentation techniques for identifying neurons in these feature rich datasets are required. However, the extremely anisotropic resolution of the data makes segmentation and tracking across slices difficult. Furthermore, the thickness of the slices can make the membranes of the neurons hard to identify. Similarly, structures can change significantly from one section to the next due to slice thickness which makes tracking difficult. This thesis presents a complete method for segmenting many neurons at once in two-dimensional (2D) electron microscopy images and reconstructing and visualizing them in three-dimensions (3D). First, we present an advanced method for identifying neuron membranes in 2D, necessary for whole neuron segmentation, using a machine learning approach. The method described uses a series of artificial neural networks (ANNs) in a framework combined with a feature vector that is composed of image and context; intensities sampled over a stencil neighborhood. Several ANNs are applied in series allowing each ANN to use the classification context; provided by the previous network to improve detection accuracy. To improve the membrane detection, we use information from a nonlinear alignment of sequential learned membrane images in a final ANN that improves membrane detection in each section. The final output, the detected membranes, are used to obtain 2D segmentations of all the neurons in an image. We also present a method that constructs 3D neuron representations by formulating the problem of finding paths through sets of sections as an optimal path computation, which applies a cost function to the identification of a cell from one section to the next and solves this optimization problem using Dijkstras algorithm. This basic formulation accounts for variability or inconsistencies between sections and prioritizes cells based on the evidence of their connectivity. Finally, we present a tool that combines these techniques with a visual user interface that enables users to quickly segment whole neurons in large volumes

A comparative analysis of machine learning approaches for plant disease identification

Background: The problems to leaf in plants are very severe and they usually shorten the lifespan of plants. Leaf diseases are mainly caused due to three types of attacks including viral, bacterial or fungal. Diseased leaves reduce the crop production and affect the agricultural economy. Since agriculture plays a vital role in the economy, thus effective mechanism is required to detect the problem in early stages.Methods: Traditional approaches used for the identification of diseased plants are based on field visits which is time consuming and tedious. In this paper a comparative analysis of machine learning approaches has been presented for the identification of healthy and non-healthy plant leaves. For experimental purpose three different types of plant leaves have been selected namely, cabbage, citrus and sorghum. In order to classify healthy and non-healthy plant leaves color based features such as pixels, statistical features such as mean, standard deviation, min, max and descriptors such as Histogram of Oriented Gradients (HOG) have been used.Results:  382 images of cabbage, 539 images of citrus and 262 images of sorghum were used as the primary dataset. The 40% data was utilized for testing and 60% were used for training which consisted of both healthy and damaged leaves. The results showed that random forest classifier is the best machine method for classification of healthy and diseased plant leaves.Conclusion:  From the extensive experimentation it is concluded that features such as color information, statistical distribution and histogram of gradients provides sufficient clue for the classification of healthy and non-healthy plants

Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue