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cnn  (Kaggle Inc)

 
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    Structured Review

    Kaggle Inc cnn
    Dataset selection, preprocessing, and Deep Learning classification. It illustrates the main steps of the method, beginning with dataset selection followed by preprocessing, including image cropping, resizing, and contrast enhancement. Subsequently, three Deep Learning (DL) systems (Convolutional Neural Network <t>(CNN),</t> Decision Tree, and Logistic Regression) were chosen for the training and classification of DR. The results were then statistically analyzed using evaluation metrics such as accuracy, sensitivity, specificity, Area Under the Curve (AUC), and the confusion matrix.
    Cnn, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/cnns/cnn/pmc13263389-41-8-21
    Average 86 stars, based on 1 article reviews
    cnn - by Bioz Stars, 2026-10
    86/100 stars

    Images

    1) Product Images from "Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification"

    Article Title: Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification

    Journal: Journal of Biomedical Physics & Engineering

    doi: 10.31661/jbpe.v0i0.2406-1774

    Dataset selection, preprocessing, and Deep Learning classification. It illustrates the main steps of the method, beginning with dataset selection followed by preprocessing, including image cropping, resizing, and contrast enhancement. Subsequently, three Deep Learning (DL) systems (Convolutional Neural Network (CNN), Decision Tree, and Logistic Regression) were chosen for the training and classification of DR. The results were then statistically analyzed using evaluation metrics such as accuracy, sensitivity, specificity, Area Under the Curve (AUC), and the confusion matrix.
    Figure Legend Snippet: Dataset selection, preprocessing, and Deep Learning classification. It illustrates the main steps of the method, beginning with dataset selection followed by preprocessing, including image cropping, resizing, and contrast enhancement. Subsequently, three Deep Learning (DL) systems (Convolutional Neural Network (CNN), Decision Tree, and Logistic Regression) were chosen for the training and classification of DR. The results were then statistically analyzed using evaluation metrics such as accuracy, sensitivity, specificity, Area Under the Curve (AUC), and the confusion matrix.

    Techniques Used: Selection

    Schematic representation of Convolutional Neural Network (CNN) architecture shows the sequence of four filtration layers (convolutional layer, rectified linear unit layer, maxpooling layer, fully connected layer, and SoftMax layer).
    Figure Legend Snippet: Schematic representation of Convolutional Neural Network (CNN) architecture shows the sequence of four filtration layers (convolutional layer, rectified linear unit layer, maxpooling layer, fully connected layer, and SoftMax layer).

    Techniques Used: Sequencing, Filtration

    Confusion matrixes of the Iraqi dataset. ( a ) using the decision tree model. ( b ) by Convolutional Neural Networks (CNN) model. ( c ) with Logistic Regression. True class data were collected based on the physician’s diagnosis. (PDR: Proliferative Diabetic Retinopathy)
    Figure Legend Snippet: Confusion matrixes of the Iraqi dataset. ( a ) using the decision tree model. ( b ) by Convolutional Neural Networks (CNN) model. ( c ) with Logistic Regression. True class data were collected based on the physician’s diagnosis. (PDR: Proliferative Diabetic Retinopathy)

    Techniques Used: Biomarker Discovery

    Confusion matrix of EyePACS ( a ) using decision tree model. ( b ) by logistic regression model. ( c ) with Convolutional Neural Network (CNN) model. (PDR: Proliferative Diabetic Retinopathy)
    Figure Legend Snippet: Confusion matrix of EyePACS ( a ) using decision tree model. ( b ) by logistic regression model. ( c ) with Convolutional Neural Network (CNN) model. (PDR: Proliferative Diabetic Retinopathy)

    Techniques Used:

    Confusion matrices of IDRiD, using ( a ) decision tree model, () logistic regression model. ( c ) Convolutional Neural Network (CNN) model. (PDR: Proliferative Diabetic Retinopathy)
    Figure Legend Snippet: Confusion matrices of IDRiD, using ( a ) decision tree model, () logistic regression model. ( c ) Convolutional Neural Network (CNN) model. (PDR: Proliferative Diabetic Retinopathy)

    Techniques Used:

    Related Articles

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    Article Snippet: Choudrie et al. , 2024 , ML and DL for Potato Disease Detection , Exceptional performance of CNNs on Kaggle’s Plant Village Dataset < br >—Promising tool for plant disease identification , Potato disease detection.

    Article Title: Optimized CNN-based ensemble deep learning approach for potato leaf disease detection with data augmentation.
    Article Snippet: In this study, we present the results of an investigation on the relative effectiveness of CNNs for the classification of potato infections by using the dataset from the Plant Village database hosted on Kaggle.

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    Article Snippet: Kang et al. (2021) , Kaggle , 2 , Pre-Trained CNNs + ML Classifiers , 0.9817% with MnasNet + FC.

    Article Title: An explainable embedded neural system for on-board ship detection from optical satellite imagery
    Article Snippet: Table 2 reports the comparative results of the custom CNNs trained and tested on the revised Kaggle dataset of ‘‘Ships in Satellite Imagery’’ (Section 3.1).

    Article Title: Localization and tracking of beluga whales in aerial video using deep learning
    Article Snippet: For instance, Bogucki et al. (2018) organized a crowdsourcing competition on Kaggle to automate the detection of North Atlantic Right whales (Eubalaena glacialis), achieving 87% precision using CNNs.

    Article Title: Machine Learning Models for Indoor Positioning Using Bluetooth RSSI and Video Data: A Case Study
    Article Snippet: For homogeneity, all CNNs obey the following conditions: all were obtained from Google’s Kaggle repository [ ]; all were pre-trained on the ImageNet ILSVRC-2012 dataset [ ]; and all have a 224 × 224 × 3 input shape dimension.

    Article Title: Machine Learning Models for Indoor Positioning Using Bluetooth RSSI and Video Data: A Case Study.
    Article Snippet: For homogeneity, all CNNs obey the following conditions: all were obtained from Google’s Kaggle repository [42]; all were pre-trained on the ImageNet ILSVRC-2012 dataset [43]; and all have a 224 × 224 × 3 input shape dimension.

    Article Title: Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern
    Article Snippet: Comparative analysis with established CNNs (VGG16 and ResNet50) on Kaggle AD datasets validates the method's effectiveness across different disease stages, contributing to advancements in AD classification.



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    Image Search Results


    Dataset selection, preprocessing, and Deep Learning classification. It illustrates the main steps of the method, beginning with dataset selection followed by preprocessing, including image cropping, resizing, and contrast enhancement. Subsequently, three Deep Learning (DL) systems (Convolutional Neural Network (CNN), Decision Tree, and Logistic Regression) were chosen for the training and classification of DR. The results were then statistically analyzed using evaluation metrics such as accuracy, sensitivity, specificity, Area Under the Curve (AUC), and the confusion matrix.

    Journal: Journal of Biomedical Physics & Engineering

    Article Title: Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification

    doi: 10.31661/jbpe.v0i0.2406-1774

    Figure Lengend Snippet: Dataset selection, preprocessing, and Deep Learning classification. It illustrates the main steps of the method, beginning with dataset selection followed by preprocessing, including image cropping, resizing, and contrast enhancement. Subsequently, three Deep Learning (DL) systems (Convolutional Neural Network (CNN), Decision Tree, and Logistic Regression) were chosen for the training and classification of DR. The results were then statistically analyzed using evaluation metrics such as accuracy, sensitivity, specificity, Area Under the Curve (AUC), and the confusion matrix.

    Article Snippet: In another investigation, Adem [ ] used a CNN built on DL methods to classify retinal fundus images from the public Kaggle dataset; there was a 75% accuracy, 95% specificity, and 93% sensitivity in DR detection.

    Techniques: Selection

    Schematic representation of Convolutional Neural Network (CNN) architecture shows the sequence of four filtration layers (convolutional layer, rectified linear unit layer, maxpooling layer, fully connected layer, and SoftMax layer).

    Journal: Journal of Biomedical Physics & Engineering

    Article Title: Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification

    doi: 10.31661/jbpe.v0i0.2406-1774

    Figure Lengend Snippet: Schematic representation of Convolutional Neural Network (CNN) architecture shows the sequence of four filtration layers (convolutional layer, rectified linear unit layer, maxpooling layer, fully connected layer, and SoftMax layer).

    Article Snippet: In another investigation, Adem [ ] used a CNN built on DL methods to classify retinal fundus images from the public Kaggle dataset; there was a 75% accuracy, 95% specificity, and 93% sensitivity in DR detection.

    Techniques: Sequencing, Filtration

    Confusion matrixes of the Iraqi dataset. ( a ) using the decision tree model. ( b ) by Convolutional Neural Networks (CNN) model. ( c ) with Logistic Regression. True class data were collected based on the physician’s diagnosis. (PDR: Proliferative Diabetic Retinopathy)

    Journal: Journal of Biomedical Physics & Engineering

    Article Title: Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification

    doi: 10.31661/jbpe.v0i0.2406-1774

    Figure Lengend Snippet: Confusion matrixes of the Iraqi dataset. ( a ) using the decision tree model. ( b ) by Convolutional Neural Networks (CNN) model. ( c ) with Logistic Regression. True class data were collected based on the physician’s diagnosis. (PDR: Proliferative Diabetic Retinopathy)

    Article Snippet: In another investigation, Adem [ ] used a CNN built on DL methods to classify retinal fundus images from the public Kaggle dataset; there was a 75% accuracy, 95% specificity, and 93% sensitivity in DR detection.

    Techniques: Biomarker Discovery

    Confusion matrix of EyePACS ( a ) using decision tree model. ( b ) by logistic regression model. ( c ) with Convolutional Neural Network (CNN) model. (PDR: Proliferative Diabetic Retinopathy)

    Journal: Journal of Biomedical Physics & Engineering

    Article Title: Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification

    doi: 10.31661/jbpe.v0i0.2406-1774

    Figure Lengend Snippet: Confusion matrix of EyePACS ( a ) using decision tree model. ( b ) by logistic regression model. ( c ) with Convolutional Neural Network (CNN) model. (PDR: Proliferative Diabetic Retinopathy)

    Article Snippet: In another investigation, Adem [ ] used a CNN built on DL methods to classify retinal fundus images from the public Kaggle dataset; there was a 75% accuracy, 95% specificity, and 93% sensitivity in DR detection.

    Techniques:

    Confusion matrices of IDRiD, using ( a ) decision tree model, () logistic regression model. ( c ) Convolutional Neural Network (CNN) model. (PDR: Proliferative Diabetic Retinopathy)

    Journal: Journal of Biomedical Physics & Engineering

    Article Title: Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification

    doi: 10.31661/jbpe.v0i0.2406-1774

    Figure Lengend Snippet: Confusion matrices of IDRiD, using ( a ) decision tree model, () logistic regression model. ( c ) Convolutional Neural Network (CNN) model. (PDR: Proliferative Diabetic Retinopathy)

    Article Snippet: In another investigation, Adem [ ] used a CNN built on DL methods to classify retinal fundus images from the public Kaggle dataset; there was a 75% accuracy, 95% specificity, and 93% sensitivity in DR detection.

    Techniques:

    Architecture of the CNN-GNN pipeline for colon disease classification. The presentation of a detailed, step-by-step breakdown of the CNN-GNN pipeline, with each stage visually represented, highlighting the transition from raw medical images to classification outputs. <xref ref-type=Figure 5 illustrates the stages involved in analyzing a colonoscopy image using a hybrid approach combining Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN). The diagram details the steps from input image processing to classification, including feature extraction, graph construction with K-NN, application of various GNN models, node-level embedding, global pooling, and linear classification with softmax. " width="100%" height="100%">

    Journal: Frontiers in Physiology

    Article Title: Grad-CAM based deep learning analytics for image-level colon disease classification based on graph neural networks and vision transformers

    doi: 10.3389/fphys.2026.1734299

    Figure Lengend Snippet: Architecture of the CNN-GNN pipeline for colon disease classification. The presentation of a detailed, step-by-step breakdown of the CNN-GNN pipeline, with each stage visually represented, highlighting the transition from raw medical images to classification outputs. Figure 5 illustrates the stages involved in analyzing a colonoscopy image using a hybrid approach combining Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN). The diagram details the steps from input image processing to classification, including feature extraction, graph construction with K-NN, application of various GNN models, node-level embedding, global pooling, and linear classification with softmax.

    Article Snippet: Alanazi et al ( ). showed that a simple CNN model, trained on the Kaggle H2E dataset, achieved 87% accuracy, outpacing several traditional classifiers.

    Techniques: Extraction

    Vivado block design—CNN IP integration on the Zynq-7000 (Processing System + CNN accelerator via AXI Interconnect).

    Journal: Sensors (Basel, Switzerland)

    Article Title: Multi-Chaotic HEOA for Hardware-Aware Neural Architecture Search: Brain Tumor Classification on FPGA

    doi: 10.3390/s26092822

    Figure Lengend Snippet: Vivado block design—CNN IP integration on the Zynq-7000 (Processing System + CNN accelerator via AXI Interconnect).

    Article Snippet: The IP block generated by Vivado HLS was integrated into a complete system design on the Zynq-7000 platform using Vivado IP Integrator (Xilinx, San Jose, CA, USA). illustrates the system block design, comprising the Zynq-7000 Processing System (PS), the custom CNN IP ( brain_tumor_cnn_0 ), the AXI Interconnect, and the interrupt management module ensuring bidirectional communication between the ARM processor and the FPGA hardware accelerator.

    Techniques: Blocking Assay