mendeley lbc dataset Search Results


mendeley lbc dataset  (Mendeley Ltd)
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Mendeley Ltd mendeley lbc dataset
<t>The</t> <t>confusion</t> matrix using the Mendeley <t>LBC</t> dataset for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the hybrid transformer model
Mendeley Lbc Dataset, supplied by Mendeley Ltd, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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The confusion matrix using the Mendeley LBC dataset for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the hybrid transformer model

Journal: BMC Medical Informatics and Decision Making

Article Title: A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis

doi: 10.1186/s12911-025-03250-x

Figure Lengend Snippet: The confusion matrix using the Mendeley LBC dataset for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the hybrid transformer model

Article Snippet: Fig. 3 The confusion matrix using the Mendeley LBC dataset for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the hybrid transformer model Additionally, Fig. displays the evaluation metrics for the performance of all methods of classification in identifying cervical cancer cells, utilizing the Mendeley LBC dataset.

Techniques:

The evaluation metrics for all classification performances using the Mendeley LBC dataset

Journal: BMC Medical Informatics and Decision Making

Article Title: A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis

doi: 10.1186/s12911-025-03250-x

Figure Lengend Snippet: The evaluation metrics for all classification performances using the Mendeley LBC dataset

Article Snippet: Fig. 3 The confusion matrix using the Mendeley LBC dataset for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the hybrid transformer model Additionally, Fig. displays the evaluation metrics for the performance of all methods of classification in identifying cervical cancer cells, utilizing the Mendeley LBC dataset.

Techniques:

The confusion matrix using both datasets, Mendeley LBC and SIPaKMeD, for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2 model, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the proposed transformer encoder model

Journal: BMC Medical Informatics and Decision Making

Article Title: A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis

doi: 10.1186/s12911-025-03250-x

Figure Lengend Snippet: The confusion matrix using both datasets, Mendeley LBC and SIPaKMeD, for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2 model, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the proposed transformer encoder model

Article Snippet: Fig. 3 The confusion matrix using the Mendeley LBC dataset for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the hybrid transformer model Additionally, Fig. displays the evaluation metrics for the performance of all methods of classification in identifying cervical cancer cells, utilizing the Mendeley LBC dataset.

Techniques:

The ROC of the proposed AI models using the combination of both Mendeley LBC and SIPaKMeD datasets for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2 model, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the proposed hybrid transformer encoder model

Journal: BMC Medical Informatics and Decision Making

Article Title: A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis

doi: 10.1186/s12911-025-03250-x

Figure Lengend Snippet: The ROC of the proposed AI models using the combination of both Mendeley LBC and SIPaKMeD datasets for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2 model, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the proposed hybrid transformer encoder model

Article Snippet: Fig. 3 The confusion matrix using the Mendeley LBC dataset for pre-trained and the proposed ensemble and transformer encoder models: a ) the InceptionResNetV2, b ) the DenseNet101 model, c ) the ensemble learning model, and d ) the hybrid transformer model Additionally, Fig. displays the evaluation metrics for the performance of all methods of classification in identifying cervical cancer cells, utilizing the Mendeley LBC dataset.

Techniques: