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convolutional neural network (cnn) analysis  (KNIME GmbH)

 
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    Structured Review

    KNIME GmbH convolutional neural network (cnn) analysis
    Example of overall deep learning flow. (A) All nodes for deep learning flow. (B) Import table of preprocessed dataset. (C) Divide the dataset into two, one for training and the other for testing. (D) Create a <t>convolutional</t> neural network. (E) Apply the trained model. (F) Rename the column of ROC Curve. (G) Scores the model. (H) Show area under curve. (I) Train the model.
    Convolutional Neural Network (Cnn) Analysis, supplied by KNIME GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/convolutional+neural+network+%28cnn%29+analysis/pmc07921566-32-30-6?v=KNIME+GmbH
    Average 90 stars, based on 1 article reviews
    convolutional neural network (cnn) analysis - by Bioz Stars, 2026-07
    90/100 stars

    Images

    1) Product Images from "Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform"

    Article Title: Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform

    Journal: Healthcare Informatics Research

    doi: 10.4258/hir.2021.27.1.82

    Example of overall deep learning flow. (A) All nodes for deep learning flow. (B) Import table of preprocessed dataset. (C) Divide the dataset into two, one for training and the other for testing. (D) Create a convolutional neural network. (E) Apply the trained model. (F) Rename the column of ROC Curve. (G) Scores the model. (H) Show area under curve. (I) Train the model.
    Figure Legend Snippet: Example of overall deep learning flow. (A) All nodes for deep learning flow. (B) Import table of preprocessed dataset. (C) Divide the dataset into two, one for training and the other for testing. (D) Create a convolutional neural network. (E) Apply the trained model. (F) Rename the column of ROC Curve. (G) Scores the model. (H) Show area under curve. (I) Train the model.

    Techniques Used:

    Architecture of the simple convolutional neural network algorithm used in the study.
    Figure Legend Snippet: Architecture of the simple convolutional neural network algorithm used in the study.

    Techniques Used:

    Performance of a simple convolutional neural network (CNN) model in detecting COVID-19: “Scorer” node shows (A) accuracy statistics (true positive, false positive, true negative, false negative, sensitivity, specificity, F-measure, accuracy and Cohen’s kappa; (B) confusion matrix (accuracy, Cohen’s kappa). (C) “ROC Curve” node shows area under the curve.
    Figure Legend Snippet: Performance of a simple convolutional neural network (CNN) model in detecting COVID-19: “Scorer” node shows (A) accuracy statistics (true positive, false positive, true negative, false negative, sensitivity, specificity, F-measure, accuracy and Cohen’s kappa; (B) confusion matrix (accuracy, Cohen’s kappa). (C) “ROC Curve” node shows area under the curve.

    Techniques Used:



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    convolutional neural network (cnn) analysis  (KNIME GmbH)
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    Example of overall deep learning flow. (A) All nodes for deep learning flow. (B) Import table of preprocessed dataset. (C) Divide the dataset into two, one for training and the other for testing. (D) Create a <t>convolutional</t> neural network. (E) Apply the trained model. (F) Rename the column of ROC Curve. (G) Scores the model. (H) Show area under curve. (I) Train the model.
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    Image Search Results


    Example of overall deep learning flow. (A) All nodes for deep learning flow. (B) Import table of preprocessed dataset. (C) Divide the dataset into two, one for training and the other for testing. (D) Create a convolutional neural network. (E) Apply the trained model. (F) Rename the column of ROC Curve. (G) Scores the model. (H) Show area under curve. (I) Train the model.

    Journal: Healthcare Informatics Research

    Article Title: Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform

    doi: 10.4258/hir.2021.27.1.82

    Figure Lengend Snippet: Example of overall deep learning flow. (A) All nodes for deep learning flow. (B) Import table of preprocessed dataset. (C) Divide the dataset into two, one for training and the other for testing. (D) Create a convolutional neural network. (E) Apply the trained model. (F) Rename the column of ROC Curve. (G) Scores the model. (H) Show area under curve. (I) Train the model.

    Article Snippet: In this paper, we introduce the KNIME analytics platform, which addresses the shortcomings of these existing tools and is capable of performing state-of-the-art deep learning algorithms [ ], such as convolutional neural network (CNN) analysis.

    Techniques:

    Architecture of the simple convolutional neural network algorithm used in the study.

    Journal: Healthcare Informatics Research

    Article Title: Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform

    doi: 10.4258/hir.2021.27.1.82

    Figure Lengend Snippet: Architecture of the simple convolutional neural network algorithm used in the study.

    Article Snippet: In this paper, we introduce the KNIME analytics platform, which addresses the shortcomings of these existing tools and is capable of performing state-of-the-art deep learning algorithms [ ], such as convolutional neural network (CNN) analysis.

    Techniques:

    Performance of a simple convolutional neural network (CNN) model in detecting COVID-19: “Scorer” node shows (A) accuracy statistics (true positive, false positive, true negative, false negative, sensitivity, specificity, F-measure, accuracy and Cohen’s kappa; (B) confusion matrix (accuracy, Cohen’s kappa). (C) “ROC Curve” node shows area under the curve.

    Journal: Healthcare Informatics Research

    Article Title: Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform

    doi: 10.4258/hir.2021.27.1.82

    Figure Lengend Snippet: Performance of a simple convolutional neural network (CNN) model in detecting COVID-19: “Scorer” node shows (A) accuracy statistics (true positive, false positive, true negative, false negative, sensitivity, specificity, F-measure, accuracy and Cohen’s kappa; (B) confusion matrix (accuracy, Cohen’s kappa). (C) “ROC Curve” node shows area under the curve.

    Article Snippet: In this paper, we introduce the KNIME analytics platform, which addresses the shortcomings of these existing tools and is capable of performing state-of-the-art deep learning algorithms [ ], such as convolutional neural network (CNN) analysis.

    Techniques: