<p>The model’s performance in classifying the colonies was based on (A) morphological, (B) textural, and (C) combined features.</p
<p><b>(A)</b> Graphical display of the ranking of ANN model performance as measured by mean ROC AUC ...
(A) Evaluation in three indicators: Accuracy, F1-score, and ROC-AUC score. (B) Confusion matrix of t...
<p>Architecture as 200, 21, 4 describes one layer with 200 filters, filter length 21 and pooling siz...
<p>Five-fold cross-validation of the performance of the proposed V-CNN model and SVM classifier in c...
Performance comparison of CNN models with different region sizes and other baseline models.</p
Receiver Operating Characteristic curve for our CNN model and the transfer-learned Inception v3 mode...
Classification performances obtained with four CNN models for the real-world testing dataset.</p
(CNN: Convolutional Neural Networks, SVM: Support Vector Machine, LMT: Logistic Model Trees).</p
Object identification is essential in diverse automated applications such as in health, business, an...
Object identification is essential in diverse automated applications such as in health, business, an...
<p>All Features denotes the performance of a model trained as linear ensemble of models trained on i...
Recurrent CNNs (a-c) were used as feature extractors in the classification task. (a, c) Feedforwards...
(A, B). Gray curves show the Pearson correlation coefficients between the mean neural distances and ...
<p>The left three models directly classify from text, the right two models are concept-extraction ba...
ROC curves for the Feature Network (a), the Topographic Map Network (b), the Short-time Fourier Tran...
<p><b>(A)</b> Graphical display of the ranking of ANN model performance as measured by mean ROC AUC ...
(A) Evaluation in three indicators: Accuracy, F1-score, and ROC-AUC score. (B) Confusion matrix of t...
<p>Architecture as 200, 21, 4 describes one layer with 200 filters, filter length 21 and pooling siz...
<p>Five-fold cross-validation of the performance of the proposed V-CNN model and SVM classifier in c...
Performance comparison of CNN models with different region sizes and other baseline models.</p
Receiver Operating Characteristic curve for our CNN model and the transfer-learned Inception v3 mode...
Classification performances obtained with four CNN models for the real-world testing dataset.</p
(CNN: Convolutional Neural Networks, SVM: Support Vector Machine, LMT: Logistic Model Trees).</p
Object identification is essential in diverse automated applications such as in health, business, an...
Object identification is essential in diverse automated applications such as in health, business, an...
<p>All Features denotes the performance of a model trained as linear ensemble of models trained on i...
Recurrent CNNs (a-c) were used as feature extractors in the classification task. (a, c) Feedforwards...
(A, B). Gray curves show the Pearson correlation coefficients between the mean neural distances and ...
<p>The left three models directly classify from text, the right two models are concept-extraction ba...
ROC curves for the Feature Network (a), the Topographic Map Network (b), the Short-time Fourier Tran...
<p><b>(A)</b> Graphical display of the ranking of ANN model performance as measured by mean ROC AUC ...
(A) Evaluation in three indicators: Accuracy, F1-score, and ROC-AUC score. (B) Confusion matrix of t...
<p>Architecture as 200, 21, 4 describes one layer with 200 filters, filter length 21 and pooling siz...
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