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Kaggle Inc xception model trained on
Xception Model Trained On, 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/xception+model/xception/pm40647600-244-9-13
Average 90 stars, based on 1 article reviews
xception model trained on - by Bioz Stars, 2026-09
90/100 stars

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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%.

Article Title: Detection of Gender in Crowds Using ResNet Model
Article Snippet: Introducing a Deep Learning (DL) strategy using the Xception model, this study trained on a dataset of 26,000 images from Kaggle.

Article Title: Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey.
Article Snippet: The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO).. The virus first originated in Wuhan (China) and rapidly affected most of the world’s population.. This outbreak’s impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls.

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%.



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A summary of DR prescreening techniques and reported performances.

Journal: Scientific Reports

Article Title: Diabetic retinopathy detection via exudates and hemorrhages segmentation using iterative NICK thresholding, watershed, and Chi 2 feature ranking

doi: 10.1038/s41598-025-90048-6

Figure Lengend Snippet: A summary of DR prescreening techniques and reported performances.

Article Snippet: Bhuiyan et al. , The cloud-based screening model was developed. Neural network architectures and logistic model trees: Xception, Inception-V3, Inception-Resnet-V2 and Logistic model trees (LMT) were employed , 88,702 images from Kaggle dataset , Sensitivity , 99.21.

Techniques: Biomarker Discovery, Plasmid Preparation, Diagnostic Assay, Software, Extraction, Selection