… In this research, we proposed a classification model based on the EfficientNet-B3 … the
learning rate (LR). We set up a custom LR that compared the loss value and training accuracy at …

… a deep learning algorithm using EfficientNet B3 for lung … The proposed approach is build
based on EfficientNet B3 model to … neural networks, particularly EfficientNet B3, in supporting …

… from CT images and machine learning. Our system is based on a new family of CNN models
called EfficientNet. In particular, we modify the Efficientnet-B3 model by removing the top …

… novel proposed model EfficientNet-B3-Attn-2. 3) We test the proposed EfficientNet-B3-Attn-2 …
In particular, we use the advanced optimization algorithm Adam with all of its parameters set …

… in the use of deep learning algorithms. Before today, deep learning algorithms have been
… Epochs: Models such as EfficientNet B3 with 12 million parameters and training datasets in …

… A significant goal of this project is to improve sturdy algorithms that may perform within the …
: EfficientNet-B3, EfficientNet-B4, and EfficientNet-B5 [10]. In the final solution, EfficientNet-B3 …

… In the performed experiment using a k-means algorithm, it achieves 100% accuracy from …
decay for learning rate with batch size of 16for EfficientNet B3-UNet model. The learning rate …

… -based and LAB-based hybrid segmentation algorithm in the image segmentation phase and
… In addition, B3, B4 and B7 models only have 1 misclassification in the augmented dataset, …

… The findings highlight the ability of deep learning algorithms to make more accurate lung
cancer diagnoses, which might lead to improvements in medical care and perhaps lower death …

… In this paper, by comparing the model performance of EfficientNet-B0-B7 (Table 4) and
EfficientNet-B4 with some other typical algorithms for plant disease classification (Table 7). It is …