Category: Image Classification and Categorization

Introduction Microglial cells are resident macrophages of myeloid origin in the central nervous system, which were introduced by Pío del Río-Hortega (Río-Hortega, 1919a,b,c,d; Sierra et al., 2016). He described the distribution and morphological phenotype of microglia and also recognized that ramified cells transformed into rod or amoeboid microglia in different situations of brain disease or pathology. In the normal brain they have been considered as “resting microglia”, but recent findings point that microglia are the most sensitive sensors in the brain, constantly scanning the parenchyma environment as surveillant cells (Nimmerjahn…

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1. Introduction In recent years, computer vision approaches based on machine learning (ML) and, in particular, those based on deep convolutional neural networks have demonstrated significant performance improvements over conventional approaches for image classification and annotation (Krizhevsky et al., 2012; Tan and Le, 2019; Zhai et al., 2021). However, these algorithms generally require a large and diverse set of annotated data to generate accurate classifications. Large amounts of annotated data are not always available, especially for tasks where producing high-quality meta-data is costly, such as image-based medical diagnosis (Cheplygina et…

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To a first-time viewer, the Tour de France can be a minefield. The winner is not simply decided by which rider crosses the finish line first. How can a rider win multiple stages and not wear the yellow jersey? What is that polka dot jersey about? And what’s with all the jargon they use? Here’s our full guide to how the Tour de France is won: the classifications, the jerseys and the previous winners. Tour de France classifications explained – what do the different jersey colours mean? The four leaders…

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Abstract Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the deep neural network (DNN) with a modified metaheuristic algorithm known as novel tuna swarm optimization (NTSO) for the purpose of sports image classification. The DNN is a potent technique capable of extracting high-level features from raw images, while the NTSO algorithm optimizes the hyperparameters of the DNN, including the number of layers, neurons, and activation functions. Through the…

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Machine learning is a broad field that uses automated training techniques to discover better algorithms. The term was coined by Arthur Samuel at IBM in 1959. A subset of AI, machine learning encompasses many techniques. An example is deep learning, an approach which relies on artificial neural networks to learn. There are many other kinds of machine learning techniques commonly used in practice, including some that are used to train deep learning algorithms. Practitioners often choose from four main types of machine learning models based on their respective suitability to…

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