custom cnn based matlab program Search Results


2d-cnn  (MathWorks Inc)
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MathWorks Inc 2d-cnn
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built-in convolutional neural network (cnn)  (MathWorks Inc)
90
MathWorks Inc built-in convolutional neural network (cnn)
Built In Convolutional Neural Network (Cnn), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cnn matlab r2018a  (MathWorks Inc)
90
MathWorks Inc cnn matlab r2018a
Cnn Matlab R2018a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab r2021b  (MathWorks Inc)
90
MathWorks Inc matlab r2021b
Matlab R2021b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab r2020b  (MathWorks Inc)
90
MathWorks Inc matlab r2020b
Matlab R2020b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cnn model  (MathWorks Inc)
90
MathWorks Inc cnn model
The architecture of the proposed <t>CNN</t> <t>model.</t>
Cnn Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cnn model - by Bioz Stars, 2026-03
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radiomics matlab package  (MathWorks Inc)
90
MathWorks Inc radiomics matlab package
The architecture of the proposed <t>CNN</t> <t>model.</t>
Radiomics Matlab Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab r2022a  (MathWorks Inc)
90
MathWorks Inc matlab r2022a
The architecture of the proposed <t>CNN</t> <t>model.</t>
Matlab R2022a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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r2021b deep learning toolbox  (MathWorks Inc)
96
MathWorks Inc r2021b deep learning toolbox
The architecture of the proposed <t>CNN</t> <t>model.</t>
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matlab 2022a  (MathWorks Inc)
90
MathWorks Inc matlab 2022a
The architecture of the proposed <t>CNN</t> <t>model.</t>
Matlab 2022a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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matlab r2020a  (MathWorks Inc)
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The architecture of the proposed <t>CNN</t> <t>model.</t>
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custom cnn based matlab program  (MathWorks Inc)
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MathWorks Inc custom cnn based matlab program
The architecture of the proposed <t>CNN</t> <t>model.</t>
Custom Cnn Based Matlab Program, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


The architecture of the proposed CNN model.

Journal: Diagnostics

Article Title: Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

doi: 10.3390/diagnostics13193155

Figure Lengend Snippet: The architecture of the proposed CNN model.

Article Snippet: The CNN model with data augmentation was employed by Asmaria et al. [ ] as one of the strategies to enhance the performance of the model.They used MATLAB to build the CNN model. Their model performs well in classifying osteosarcoma, and the accuracy reaches 95.37%.

Techniques:

Training and validation accuracy curve of the proposed CNN model.

Journal: Diagnostics

Article Title: Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

doi: 10.3390/diagnostics13193155

Figure Lengend Snippet: Training and validation accuracy curve of the proposed CNN model.

Article Snippet: The CNN model with data augmentation was employed by Asmaria et al. [ ] as one of the strategies to enhance the performance of the model.They used MATLAB to build the CNN model. Their model performs well in classifying osteosarcoma, and the accuracy reaches 95.37%.

Techniques: Biomarker Discovery

Training and validation loss curve of proposed CNN model.

Journal: Diagnostics

Article Title: Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

doi: 10.3390/diagnostics13193155

Figure Lengend Snippet: Training and validation loss curve of proposed CNN model.

Article Snippet: The CNN model with data augmentation was employed by Asmaria et al. [ ] as one of the strategies to enhance the performance of the model.They used MATLAB to build the CNN model. Their model performs well in classifying osteosarcoma, and the accuracy reaches 95.37%.

Techniques: Biomarker Discovery

Confusion matrix of the proposed CNN model.

Journal: Diagnostics

Article Title: Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

doi: 10.3390/diagnostics13193155

Figure Lengend Snippet: Confusion matrix of the proposed CNN model.

Article Snippet: The CNN model with data augmentation was employed by Asmaria et al. [ ] as one of the strategies to enhance the performance of the model.They used MATLAB to build the CNN model. Their model performs well in classifying osteosarcoma, and the accuracy reaches 95.37%.

Techniques:

Class-wise Accuracy (%), Precision (%), Recall (%), F1-Score (%), AUC of the proposed CNN model on Balanced Dataset.

Journal: Diagnostics

Article Title: Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

doi: 10.3390/diagnostics13193155

Figure Lengend Snippet: Class-wise Accuracy (%), Precision (%), Recall (%), F1-Score (%), AUC of the proposed CNN model on Balanced Dataset.

Article Snippet: The CNN model with data augmentation was employed by Asmaria et al. [ ] as one of the strategies to enhance the performance of the model.They used MATLAB to build the CNN model. Their model performs well in classifying osteosarcoma, and the accuracy reaches 95.37%.

Techniques:

Class-wise accuracy (%), precision (%), recall (%), f1-score (%), and AUC score 603 (%) of the proposed CNN model on a balanced dataset.

Journal: Diagnostics

Article Title: Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

doi: 10.3390/diagnostics13193155

Figure Lengend Snippet: Class-wise accuracy (%), precision (%), recall (%), f1-score (%), and AUC score 603 (%) of the proposed CNN model on a balanced dataset.

Article Snippet: The CNN model with data augmentation was employed by Asmaria et al. [ ] as one of the strategies to enhance the performance of the model.They used MATLAB to build the CNN model. Their model performs well in classifying osteosarcoma, and the accuracy reaches 95.37%.

Techniques:

AUC ROC (Receiver Operating Characteristic) curve of proposed CNN model.

Journal: Diagnostics

Article Title: Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

doi: 10.3390/diagnostics13193155

Figure Lengend Snippet: AUC ROC (Receiver Operating Characteristic) curve of proposed CNN model.

Article Snippet: The CNN model with data augmentation was employed by Asmaria et al. [ ] as one of the strategies to enhance the performance of the model.They used MATLAB to build the CNN model. Their model performs well in classifying osteosarcoma, and the accuracy reaches 95.37%.

Techniques:

Comparison of class-wise precision (%), Recall (%), and F1-Score (%) of the proposed CNN model and ENL-CNE model on the balanced training set.

Journal: Diagnostics

Article Title: Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

doi: 10.3390/diagnostics13193155

Figure Lengend Snippet: Comparison of class-wise precision (%), Recall (%), and F1-Score (%) of the proposed CNN model and ENL-CNE model on the balanced training set.

Article Snippet: The CNN model with data augmentation was employed by Asmaria et al. [ ] as one of the strategies to enhance the performance of the model.They used MATLAB to build the CNN model. Their model performs well in classifying osteosarcoma, and the accuracy reaches 95.37%.

Techniques: Comparison