A dilated CNN model for image classification 来自 IEEEXplore 喜欢 0 阅读量: 222 作者:X Lei,H Pan,X Huang 摘要: The dilated convolution algorithm, which is widely used for image segmentation, is applied in the image classification field in this paper. In many traditional image classification ...
Lei, X., Pan, H., Huang, X.: A dilated CNN model for image classification. IEEE Access7, 124087–124095 (2019) Google Scholar Tang, S., Feng, L., Kuang, Z., Chen, Y., Zhang, W.: Fast video shot transition localization with deep structured models. In: Asian Conference on Compute...
For accurate classification of facial expressions, it is important to extract distinctive features that are unique to an expression while covering the complete image from different perspectives. Dilated convolutions broaden the receptive field and help to bring diversity to the feature maps with no incr...
SSNET: an improved deep hybrid network for hyperspectral image classification A novel deep learning framework is proposed in this paper which efficiently utilises convolutional neural network (CNN) and spatial pyramid pooling (SPP) ... A Paul,S Bhoumik,N Chaki - 《Neural Computing & Applications...
The trained model is used for classification and evaluation. 3.1. Transfer learning and fine-tuning approaches Transfer learning is the go-to method for most of the papers. Pretrained models that are trained on the ImageNet database are used to perform transfer learning. Although the method is ...
Full size image Immunohistochemistry sections are often counterstained with an unspecific cellular marker (like Nissl, hematoxylin). For analysis of SN DA neurons, such counterstains are often performed to confirm that a loss of TH-immunopositive neurons (e.g., in a PD-model) indeed corresponds...
Deep convolutional neural network (DCNN) has obtained great successes for image classification. However, the principle of human visual system (HVS) is not fully investigated and incorporated into the current popular DCNN models. In this work, a novel DCNN model named parallel crossing DCNN (PC–DC...
They are placed before the classification output of a CNN and are used to flatten the results before a prediction is made using linear classifiers. While training the CNN architecture, the model predicts the class scores for training images, computes the loss using the selected loss function and...
Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained
[68] Wang C, He T, Liu J, et al. A HEp-2 Cell Image Classification Model Based onDeep Residual Shrinkage NetworkCombined with Dilated Convolution[C]//International Conference on Intelligent Information Processing. Cham: Springer International Publishing, 2022: 409-418. ...