Introduction

Ginneken, Esther van; Hart, Emily Luise

doi:10.1057/978-1-349-95185-7_1

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…The second dataset was created as part of a series of research projects (SAMRH) directed by an expert from the Centro de Investigación en Computación of Mexican Instituto Politécnico Nacional, particularly in the thesis of García-Floriano [4]; a set of 150 images of healthy patients and patients affected by various retinopathies was generated. This set comprises images provided by an ophthalmology specialist and images taken from public repositories such as DRIVE [34].…”

Section: Datasets For This Workmentioning

confidence: 99%

Age-Related Macular Degeneration Detection in Retinal Fundus Images by a Deep Convolutional Neural Network

García-Floriano,

Ventura-Molina

2024

Mathematics

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Computer-based pre-diagnosis of diseases through medical imaging is a task worked on for many years. The so-called fundus images stand out since they do not have uniform illumination and are highly sensitive to noise. One of the diseases that can be pre-diagnosed through fundus images is age-related macular degeneration, which initially manifests as the appearance of lesions called drusen. Several ways of pre-diagnosing macular degeneration have been proposed, methods based entirely on the segmentation of drusen with prior image processing have been designed and applied, and methods based on image pre-processing and subsequent conversion to feature vectors, or patterns, to be classified by a Machine-Learning model have also been developed. Finally, in recent years, the use of Deep-Learning models, particularly Convolutional Networks, has been proposed and used in classification problems where the data are only images. The latter has allowed the so-called transfer learning, which consists of using the learning achieved in the solution of one problem to solve another. In this paper, we propose the use of transfer learning through the Xception Deep Convolutional Neural Network to detect age-related macular degeneration in fundus images. The performance of the Xception model was compared against six other state-of-the-art models with a dataset created from images available in public and private datasets, which were divided into training/validation and test; with the training/validation set, the training was made using 10-fold cross-validation. The results show that the Xception neural network obtained a validation accuracy that surpasses other models, such as the VGG-16 or VGG-19 networks, and had an accuracy higher than 80% in the test set. We consider that the contributions of this work include the use of a Convolutional Neural Network model for the detection of age-related macular degeneration through the classification of fundus images in those affected by AMD (drusen) and the images of healthy patients. The performance of this model is compared against other methods featured in the state-of-the-art approaches, and the best model is tested on a test set outside the training and validation set.

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Section: Datasets For This Workmentioning

confidence: 99%

Age-Related Macular Degeneration Detection in Retinal Fundus Images by a Deep Convolutional Neural Network

García-Floriano,

Ventura-Molina

2024

Mathematics

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“…All of these developments were in the context of emerging evidence that the criminal justice system and police were ineffective in reducing crime, diminished faith in offender rehabilitation and weakened state and welfare provision and a socio-political climate where new-right politics was flourishing (van Ginneken, 2017). By the mid-1980s, the Five Towns Initiative (1986) Whilst the above provides a very brief background context to crime, prevention, community safety and multi-agency partnerships, taking into account the political climate in which developments took place, it is important to capture the extent to which these developments were exclusive to particular types of safety and security threat concerns and to particular sources of such threats.…”

Section: Background Context: Crime Prevention Community Safety and Mu...mentioning

confidence: 99%

Partnerships and Activism: Community Safety, Multi-agency Partnerships and Safeguarding Victims

Davies

2020

Victimology

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“…[7] As we age, the reduced organ function, including the vascular structures and heart function, require an increased supply of nutrients to maintain body function. [2] These changes are assumed to be sensed and reciprocated by changes in microbiome species composition and metabolic capabilities. Simply put, our host-associated microbial communities serve as accessory genetic reservoirs that can aid the "holobiont" (i.e., the multipartite entity of a host and its associated microbial communities), [8] as well as the microbes.…”

Section: Is There a Link Between The Microbiome And Aging?mentioning

confidence: 99%

“…This is, of course, subject to individual variation. [2] This illustrates the network of increasing impairment on the functional pathways and reduced interorgan crosstalk that collectively is associated with aging.…”

Section: Introduction: What Is Aging?mentioning

confidence: 96%

“…Interestingly, the aging process is characterized by a gradual loss of function and occurs as a consequence of several steps of dysfunction in the body. [1] In a recent review, van Ginneken [2] elegantly described the gradual loss of function with age. For example, by the age of 65 one has an approximate 25% reduction in cardiac output and almost 50% reduction in respiration capacity.…”

Section: Introduction: What Is Aging?mentioning

confidence: 99%

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The Microbiome Mediates Environmental Effects on Aging

et al. 2019

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Humans' indigenous microbes strongly influence organ functions in an age-and diet-dependent manner, adding an important dimension to aging biology that remains poorly understood. Although age-related differences in the gut microbiota composition correlate with age-related loss of organ function and diseases, including inflammation and frailty, variation exists among the elderly, especially centenarians and people living in areas of extreme longevity. Studies using shortlived as well as nonsenescent model organisms provide surprising functional insights into factors affecting aging and implicate attenuating effects of microbes as well as a crucial role for certain transcription factors like forkhead box O. The unexpected beneficial effects of microbes on aged animals imply an even more complex interplay between the gut microbiome and the host. The microbiome constitutes the major interface between humans and the environment, is influenced by biosocial stressors and behaviors, and mediates effects on health and aging processes, while being moderated by sex and developmental stages.

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Introduction

Cited by 4 publications

References 3 publications

Age-Related Macular Degeneration Detection in Retinal Fundus Images by a Deep Convolutional Neural Network

Age-Related Macular Degeneration Detection in Retinal Fundus Images by a Deep Convolutional Neural Network

Partnerships and Activism: Community Safety, Multi-agency Partnerships and Safeguarding Victims

The Microbiome Mediates Environmental Effects on Aging

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