alexnet (SoftMax Inc)
90
Structured Review
SoftMax Inc
alexnet
Alexnet, supplied by SoftMax 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/alexnet++softmax/alexnet/pmc10340497-4-0-19
Average 90 stars, based on 1 article reviews
Alexnet, supplied by SoftMax 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/alexnet++softmax/alexnet/pmc10340497-4-0-19
Average 90 stars, based on 1 article reviews
alexnet - by Bioz Stars,
2026-10
90/100 stars
Images
1) Product Images from "Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing"
Article Title: Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing
Journal: Diagnostics
doi: 10.3390/diagnostics13132191
Figure Legend Snippet: Configuration of various TL approaches taken into account in this work.
Techniques Used:
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Comparison:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Biomarker Discovery:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Modification:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Cytometry:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Flow Cytometry:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Transformation Assay:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Extraction:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Standard Deviation:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Derivative Assay:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Selection:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Plasmid Preparation:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Activation Assay:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Microscopy:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Construct:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Diagnostic Assay:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Disruption:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Produced:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Introduce:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Shear:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Microarray:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Cell Counting:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Isolation:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Wright Stain:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Labeling:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , Software:Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: , 80 , 24,000 , Article Title: A lightweight convolutional neural network (CNN) model for diatom classification: DiatomNet Article Snippet: Proposed work , 68 , 12,108 , Article Title: Lung Cancer Prediction using Deep Neural Networks Article Snippet: Article Title: Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images. Article Snippet: They used an AlexNet with 1000 class neurons, a rectified linear unit (ReLU), an alpha dropout layer, and a Article Title: Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification Article Snippet: Hosny et al. [ ] , 2018 , |