vgg16 inceptionv3 xception (Kaggle Inc)

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Kaggle Inc vgg16 inceptionv3 xception
Vgg16 Inceptionv3 Xception, 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/inceptionv3/resnet50/pmc12567645-5-2-15
Average 86 stars, based on 1 article reviews

vgg16 inceptionv3 xception - by Bioz Stars, 2026-10

86/100 stars

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Article Title: ViX-MangoEFormer: An Enhanced Vision Transformer–EfficientFormer and Stacking Ensemble Approach for Mango Leaf Disease Recognition with Explainable Artificial Intelligence
Article Snippet: Varma et al. [28] and Swapno et al. [29] applied pretrained CNNs, such as InceptionV3 and EfficientNetV2L, on a Kaggle dataset of 4000 mango leaf images, reporting accuracies of 99.87% and 96.87%, respectively.

Magnetic Resonance Imaging:

Article Title: Automatic smart brain tumor classification and prediction system using deep learning
Article Snippet: , 2024 , Kaggle , 43,475 MRI images , Glioma, Pituitary, Meningioma, No Tumor , 4 , convolutional-block-based architecture , 97.58%. .. , 2024 , Kaggle , 2870 MRI images , Glioma, Pituitary, Meningioma, No Tumor , 4 , InceptionV3, EficientNetB4, VGG16, VGG19, Multi-Layer CNN , 96%, 97%, 98%, 96%, 91%. .. , 2024 , Kaggle , 3000 MRI images , Tumor No Tumor , 2 , CNN Model , 98%.

Article Title: Deep Learning Architectures for Brain Tumor Classification
Article Snippet: .. We try different pre-trained CNN models, i.e., VGG19, ResNet101, EfficientNetB0, MobileNetV2, InceptionV3, and DenseNet121, on a Kaggle dataset of 13,196 MRI scans into four categories: pituitary, glioma, meningioma, and no tumor. ..

Plasmid Preparation:

Article Title: Diagnosis of early nitrogen, phosphorus and potassium deficiency categories in rice based on multimodal integration and knowledge distillation
Article Snippet: .. Supreetha et al. used InceptionV3, VGG16, VGG19, ResNet50 and ResNet152 along with support vector machines (SVMs) for predicting nutrient deficiencies in the Kaggle’s rice NPK deficiency dataset through data augmentation and CNN-SVM fusion comparison experiments, the optimal classification model was obtained as ResNet50 + SVM with an accuracy of 99.05%. .. Kolhar et al. trained the Xception model, Vision Transformer and a multilayer perceptron (MLP) based hybrid model and used all three models to test them on a publicly available dataset of nutrient deficiency symptoms in rice plants on Kaggle, and the accuracy of all three models exceeded 92%.

Comparison:

Modification:

Article Title: Diagnosis of early nitrogen, phosphorus and potassium deficiency categories in rice based on multimodal integration and knowledge distillation.
Article Snippet: Kolhar21 et al. trained the Xception model, Vision Transformer and a multilayer perceptron (MLP) based hybrid model and used all three models to test them on a publicly available dataset of nutrient deficiency symptoms in rice plants on Kaggle, and the accuracy of all three models exceeded 92%. .. Talukder22 et al. proposed a robust deep integrated convolutional neural network (DECNN) model, which was modified and integrated by adding various layers to the pre-trained models of InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169 and DenseNet201, to validate on the Kaggle’s dataset performance of the DECNN model with an accuracy of 98.33%. ..

Article Title: Diagnosis of early nitrogen, phosphorus and potassium deficiency categories in rice based on multimodal integration and knowledge distillation
Article Snippet: Kolhar et al. trained the Xception model, Vision Transformer and a multilayer perceptron (MLP) based hybrid model and used all three models to test them on a publicly available dataset of nutrient deficiency symptoms in rice plants on Kaggle, and the accuracy of all three models exceeded 92%. .. Talukder et al. proposed a robust deep integrated convolutional neural network (DECNN) model, which was modified and integrated by adding various layers to the pre-trained models of InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169 and DenseNet201, to validate on the Kaggle’s dataset performance of the DECNN model with an accuracy of 98.33%. ..

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