Category: Deep Learning Architectures

In a recent paper posted to the preprint repository medRxiv*, researchers investigated the potential of using deep learning algorithms for automating pneumonia detection from chest X-ray images. They compared various deep learning techniques to evaluate their effectiveness and potential use in clinical settings, aiming to enhance the reliability and accessibility of diagnostic practices. Study: Deep Learning for Pneumonia Detection in Pediatric Chest X-rays. Image Credit: Tewan Banditrakkanka/Shutterstock.com Background Related Stories Pneumonia is a significant global health concern, causing significant illness and mortality rates worldwide. Traditionally, diagnosing pneumonia relies on manual…

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1 Introduction According to the World Health Organization (2022), approximately 55 million individuals are now affected by dementia—a number expected to rise to 78 million by the year 2030. Alzheimer’s disease (AD)—the most prevalent type of dementia – accounts for around 60–70% of the overall number of cases (World Health Organization, 2022). The underlying cause of AD is linked to the abnormal accumulation of specific proteins in the brain, including beta-amyloid plaques (Jack et al., 2018). These plaques are insoluble and toxic to brain cells (Masters and Selkoe, 2012). Additionally,…

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Introduction Healthcare costs are rising, and patients need to manage their healthcare expenditures on medications (Bertsimas et al., 2008). Predicting medication cost in the future could help patients better manage patient-related healthcare expenditures (Zhao et al., 2001). To predict medication costs, one needs data concerning patients’ medicine-purchase patterns. Currently, there exist significant amounts of digital healthcare data that can provide helpful insights into healthcare expenditures, and these data could bring about positive changes in healthcare policymaking (Farley et al., 2006). Although there exist data, accessing these data is a major…

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1. Introduction We are living in the big data era where all areas of science and industry generate massive amounts of data. This confronts us with unprecedented challenges regarding their analysis and interpretation. For this reason, there is an urgent need for novel machine learning and artificial intelligence methods that can help in utilizing these data. Deep learning (DL) is such a novel methodology currently receiving much attention (Hinton et al., 2006). DL describes a family of learning algorithms rather than a single method that can be used to learn…

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1. Introduction Deep convolutional neural networks (CNNs) are the core building block for most modern visual recognition systems and lead to major breakthroughs in many domains of computer perception in the past several years. Therefore, the community has been searching the high dimensional space of possible network architectures for models with desirable properties. Important milestones such as DanNet (Ciresan et al., 2012), AlexNet (Krizhevsky et al., 2012), VGG (Simonyan and Zisserman, 2015), HighwayNet (Srivastava et al., 2015), and ResNet (He et al., 2016) (a HighwayNet with open gates) can be…

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