Showing 1,601 - 1,620 results of 8,109 for search 'computing patterns', query time: 0.15s Refine Results
  1. 1601

    Radiology of viral pneumonia by A. A. Speranskaia, L. N. Novikova, O. P. Baranova, M. A. Vasilieva

    Published 2016-07-01
    “…All patients underwent conventional X-ray examinations (radiography in two projections), multi-spiral computed tomography (CT), high-resolution CT. Seven patients underwent CT angiography. …”
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    Article
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    Enhancing medical image classification via federated learning and pre-trained model by Parvathaneni Naga Srinivasu, G. Jaya Lakshmi, Sujatha Canavoy Narahari, Jana Shafi, Jaeyoung Choi, Muhammad Fazal Ijaz

    Published 2024-09-01
    “…The Convolutional Neural Network (CNN) model with Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), along with the EfficientNet model, are being used as the local models. …”
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  4. 1604

    Evaluation of lung cancer early detection offered by the German Social Accident Insurance for formerly asbestos-exposed employees using low-dose computed tomography – setting and s... by Felix Greiner, Jan Heidrich, Helena Keller, Dirk Taeger, Thorsten Wiethege, Volker Harth

    Published 2025-07-01
    “…Abstract Background Clinical trials have shown the benefits of lung cancer screening (LCS) in certain high-risk groups using low-dose high-resolution computed tomography (LDCT). Risk groups are usually defined by age and tobacco use. …”
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    Article
  5. 1605

    Pseudoexhaustive memory testing based on March A type march tests by V. N. Yarmolik, I. Mrozek, S. V. Yarmolik

    Published 2020-06-01
    “…The relevance of testing of memory devices of modern computing systems is shown. The methods and algorithms for implementing test procedures based on classical March tests are analyzed. …”
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    Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier by Hong Zheng MS, Wei Chen MS, Jun Liu MD, Lian Jian MD, Tao Luo BS, Xiaoping Yu MD

    Published 2024-12-01
    “…Subsequently, radiomics refinement and deep learning features were employed using a machine learning algorithm to construct the RRDLC-Classifier, which aims to predict high-grade patterns in clinical stage I solid LADC. Evaluation metrics, such as receiver operating characteristic curves, areas under the curve (AUCs), accuracy, sensitivity, and specificity, were computed for assessment. …”
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    Haemoglobin concentration and survival of haemodialysis patients before and after experiencing cardiovascular disease: a cohort study from Japanese dialysis outcomes and practice p... by Tadao Akizawa, Ryo Kido, Shunichi Fukuhara

    Published 2019-09-01
    “…Adjusted hazard ratios (aHRs) were computed using a time-dependent Cox model with interaction test for cardiovascular comorbidity.Results Over a median 2.0 years, 847 all-cause and 326 cardiovascular deaths, and 1000 adverse cardiovascular events occurred. …”
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    A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Met... by A. S. Albahri, Z. T. Al-qaysi, Laith Alzubaidi, Alhamzah Alnoor, O. S. Albahri, A. H. Alamoodi, Anizah Abu Bakar

    Published 2023-01-01
    “…The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. …”
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    Evaluating sleep's role in type 2 diabetes mellitus: Evidence from NHANES by Jijun Zhang, Chuanli Yang, Jie An, Yunhe Fan, Xiushan Dong

    Published 2025-03-01
    “…Information on self-reported sleep disorder diagnosis, subjective sleep difficulties, and sleep duration was collected during in-home visits by trained interviewers using the Computer-Assisted Personal Interviewing system. The sleep pattern was derived from scoring three mentioned factors: no self-reported sleep disorder diagnosis, no subjective sleep difficulties, and sleep duration of 7–9 h were classified as low-risk (score 0), while the presence of self-reported sleep disorder diagnosis, subjective sleep difficulties, or sleep duration <7 or >9 h were classified as high-risk (score 1). …”
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