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efficientnetb7  (Kaggle Inc)

 
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    Kaggle Inc efficientnetb7
    Efficientnetb7, supplied by Kaggle Inc, 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/efficientnetb7/efficientnetb0/pm40610748-96-252-255
    Average 90 stars, based on 1 article reviews
    efficientnetb7 - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    other:

    Article Title: SmartSkin-XAI: An Interpretable Deep Learning Approach for Enhanced Skin Cancer Diagnosis in Smart Healthcare
    Article Snippet: Modified EfficientNet-B3 with Deep Transfer Learning , 90.6% , Kaggle skin cancer dataset , [ ] .

    Magnetic Resonance Imaging:

    Article Title: A novel hybrid vision UNet architecture for brain tumor segmentation and classification.
    Article Snippet: .. Recall = TN TP + FN (4) Authors Dataset Approach Results / Metrics Limitations / Future Work Mehta & Arbel et al.17 BraTS2018 3D UNet Dice scores: ET: 0.706 WT: 0.871 TC: 0.771 Need to improve testing accuracy; limited generalizability Cicek et al. (2016)18 Xenopus kidney 3D UNet from sparse annotation IoU: 0.863 Performance may vary with different dataset characteristics; sensitive to annotation quality Gitonga et al.19 BraTS2021 3D Attention-based UNet Dice Coefficient: 0.9864 Computationally intensive Asiri et al. (2023)20 TCGA-LGG, TCIA MRI ResNet50 + UNet IoU: 0.91, DSC: 0.95, SI: 0.95 Limited to LGG class Shedbalkar & Prabhushetty et al.21 Figshare MRI UNet + chopped VGGNet Accuracy: 98.93%, Sensitivity: 0.98, Precision: 0.9833, F1-score: 0.9833 Limited validation and generalization Pravitasari et al.22 Custom UNet-VGG16 Accuracy: 96.1% Need to explore different architecture Kolarik et al.24 Custom + MICCAI 2016 MRI 3D Dense-U-Net SSIM: 0.78547, PSNR: 24.09 dB Need to explore different datasets Chen et al.27 Synapse multi-organ segmentation dataset TransUNet DSC: 77.48, HD: 31.69 Need to evaluate on different dataset Wang et al.28 BraTS 2019 TransBTS Dice scores: ET: 78.92 WT: 90.23 TC: 81.19 Computationally intensive Hatamizadeh et al.29 BraTS 2021 Swin UNETR ET DSC: 0.858, HD: 6.016 WT DSC: 0.926, HD: 5.831 TC DSC: 0.885, HD: 3.770 High memory usage Cao et al.30 Synapse multi-organ segmentation dataset Swin-Unet DSC: 79.13 HD: 21.55 Pure transformer; still evolving for medical images Aloraini et al.31 BraTS 2018 and Figshare ViT-CNN Accuracy: 96.75% (BraTS), 99.10% (Figshare) Need to explore with lightweight CNN model Khushi et al.32 Multiclass brain tumor Kaggle dataset EfficientNetB7 Accuracy: 98.97% Need to evaluate on real medical image dataset Table 1. ..

    Article Title: Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.
    Article Snippet: .. Study Pathology Modality Technology Accuracy XAI Dataset [82] 2024 Brain tumor MRI T1w 10 TL frameworks Up to 98% for EfficientNetB0 Grad-CAM, Grad-CAM++, IG, and Saliency Mapping Kaggle—926 MRI images of glioma tumors, 500 with no tumors, 901 pituitary tumors, and 937 meningioma tumors [83] 2024 Brain tumor MRI ResNet50 98.52% Grad-CAM Kaggle [84] 2024 AD, MCI MRI CNNs with a multi-feature kernel supervised within-class-similar discriminative dictionary learning (MKSCDDL) 98.27% Saliency maps, Grad-CAM, Score-CAM, Grad-CAM++ ADNI [85] 2024 Brain tumor MRI Physics-informed deep learning (PIDL) 96% LIME, Grad-CAM Kaggle—glioma 1621 images, meningioma 1645, pituitary tumors 1775, and non-tumorous scans 2000 images [86] 2024 Brain tumors four classes: glioma, meningioma, no tumor, and pituitary tumors MRI VGG19 with inverted pyramid pooling module (iPPM) 99.3% LIME Kaggle—7023 images [2] 2023 PD MRI T1w CNN 79.3% Saliency maps 1024 PD patients and 1017 age and sex matched HC from 13 different studies [87] 2023 Brain tumor MRI VGG16 97.33% LRP 1500 normal brain MRI images and 1500 tumor brain MRI images—Kaggle [3] 2023 Non-dementia, very mild, mild, and moderate MRI CNN 94.96%. ..

    Biomarker Discovery:

    Article Title: A novel hybrid vision UNet architecture for brain tumor segmentation and classification.
    Article Snippet: .. Recall = TN TP + FN (4) Authors Dataset Approach Results / Metrics Limitations / Future Work Mehta & Arbel et al.17 BraTS2018 3D UNet Dice scores: ET: 0.706 WT: 0.871 TC: 0.771 Need to improve testing accuracy; limited generalizability Cicek et al. (2016)18 Xenopus kidney 3D UNet from sparse annotation IoU: 0.863 Performance may vary with different dataset characteristics; sensitive to annotation quality Gitonga et al.19 BraTS2021 3D Attention-based UNet Dice Coefficient: 0.9864 Computationally intensive Asiri et al. (2023)20 TCGA-LGG, TCIA MRI ResNet50 + UNet IoU: 0.91, DSC: 0.95, SI: 0.95 Limited to LGG class Shedbalkar & Prabhushetty et al.21 Figshare MRI UNet + chopped VGGNet Accuracy: 98.93%, Sensitivity: 0.98, Precision: 0.9833, F1-score: 0.9833 Limited validation and generalization Pravitasari et al.22 Custom UNet-VGG16 Accuracy: 96.1% Need to explore different architecture Kolarik et al.24 Custom + MICCAI 2016 MRI 3D Dense-U-Net SSIM: 0.78547, PSNR: 24.09 dB Need to explore different datasets Chen et al.27 Synapse multi-organ segmentation dataset TransUNet DSC: 77.48, HD: 31.69 Need to evaluate on different dataset Wang et al.28 BraTS 2019 TransBTS Dice scores: ET: 78.92 WT: 90.23 TC: 81.19 Computationally intensive Hatamizadeh et al.29 BraTS 2021 Swin UNETR ET DSC: 0.858, HD: 6.016 WT DSC: 0.926, HD: 5.831 TC DSC: 0.885, HD: 3.770 High memory usage Cao et al.30 Synapse multi-organ segmentation dataset Swin-Unet DSC: 79.13 HD: 21.55 Pure transformer; still evolving for medical images Aloraini et al.31 BraTS 2018 and Figshare ViT-CNN Accuracy: 96.75% (BraTS), 99.10% (Figshare) Need to explore with lightweight CNN model Khushi et al.32 Multiclass brain tumor Kaggle dataset EfficientNetB7 Accuracy: 98.97% Need to evaluate on real medical image dataset Table 1. ..

    Control:

    Article Title: Using image augmentation techniques and convolutional neural networks to identify insect infestations on tomatoes
    Article Snippet: .. [ ] , 2022 , Pest Identification and Control with AR. , Xception, MobileNet, Mo-bilenetV2 and EfficientNetB7 , Kaggle dataset , EfficientNetB7 95.15 %. ..



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    Image Search Results


    EfficientnetB7_CNN model architecture.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: EfficientnetB7_CNN model architecture.

    Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

    Techniques:

    Evaluation of the models using FER13 and FER24_CK + datasets.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: Evaluation of the models using FER13 and FER24_CK + datasets.

    Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

    Techniques:

    EfficientNetB7-CNN (implementation parameters).

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: EfficientNetB7-CNN (implementation parameters).

    Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

    Techniques:

    Confusion matrix of EfficientNetB7-CNN for FER task on FER24-CK+ (7 classes) private testing.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: Confusion matrix of EfficientNetB7-CNN for FER task on FER24-CK+ (7 classes) private testing.

    Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

    Techniques:

    Outlines the EfficientNetB7-CNN performance measure for private testing.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: Outlines the EfficientNetB7-CNN performance measure for private testing.

    Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

    Techniques:

    State-of-the-art comparison of models’ accuracy using the FER13 dataset as a base.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: State-of-the-art comparison of models’ accuracy using the FER13 dataset as a base.

    Article Snippet: To leverage (pre-trained) models accessible on the Kaggle platform, we propose a new architecture based on the EfficientNetB7 model. We initialize the base model with pre-trained weights from ImageNet and exclude the top classification layer to enable customization for our specific task.

    Techniques: Comparison

    EfficientnetB7_CNN model architecture.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: EfficientnetB7_CNN model architecture.

    Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

    Techniques:

    Evaluation of the models using FER13 and FER24_CK + datasets.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: Evaluation of the models using FER13 and FER24_CK + datasets.

    Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

    Techniques:

    EfficientNetB7-CNN (implementation parameters).

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: EfficientNetB7-CNN (implementation parameters).

    Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

    Techniques:

    Confusion matrix of EfficientNetB7-CNN for FER task on FER24-CK+ (7 classes) private testing.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: Confusion matrix of EfficientNetB7-CNN for FER task on FER24-CK+ (7 classes) private testing.

    Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

    Techniques:

    Outlines the EfficientNetB7-CNN performance measure for private testing.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: Outlines the EfficientNetB7-CNN performance measure for private testing.

    Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

    Techniques:

    State-of-the-art comparison of models’ accuracy using the FER13 dataset as a base.

    Journal: Heliyon

    Article Title: Introducing a novel dataset for facial emotion recognition and demonstrating significant enhancements in deep learning performance through pre-processing techniques

    doi: 10.1016/j.heliyon.2024.e38913

    Figure Lengend Snippet: State-of-the-art comparison of models’ accuracy using the FER13 dataset as a base.

    Article Snippet: We trained the EfficientNetB7 DL model on the Kaggle platform using 84000 samples in FER24_CK+ (10 emotions) and the T4x2 accelerator for 185 epochs.

    Techniques: Comparison