"Brahnam, Sheryl" - Selaus tekijän mukaan TUNICRIS-julkaisut
Viitteet 1-5 / 5
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Comparison of Different Convolutional Neural Network Activation Functions and Methods for Building Ensembles for Small to Midsize Medical Data Sets
(16.08.2022)
article<p>CNNs and other deep learners are now state-of-the-art in medical imaging research. However, the small sample size of many medical data sets dampens performance and results in overfitting. In some medical areas, ... -
Comparison of Different Image Data Augmentation Approaches
(27.11.2021)
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An ensemble of convolutional neural networks for audio classification
(06 / 2021)
articleResearch in sound classification and recognition is rapidly advancing in the field of pattern recognition. One important area in this field is environmental sound recognition, whether it concerns the identification of ... -
Feature transforms for image data augmentation
(2022)
articleA problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this ... -
Varied Image Data Augmentation Methods for Building Ensemble
(2023)
articleConvolutional Neural Networks (CNNs) are used in many domains but the requirement of large datasets for robust training sessions and no overfitting makes them hard to apply in medical fields and similar fields. However, ...
