In this research work, a novel dense Convolutional Neural Network (CNN) based deep learning model is proposed to accurately detect benign and malignant tumour classes using Wisconsin Breast Cancer dataset. The performance metrics such as accuracy, sensitivity, precision, F1-score, a...
论文信息: A mixed-scale dense convolutional neural network for image analysis, 2017年12月发表在美国国家科学院院刊。 参考文献: [1]Pelt D M, Sethian J A. A mixed-scale dense convolutional neural network for image analysis. In proceedings of the National Academy of Sciences, 2017. [2] Yu F...
Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the conte...
[2]D. Eigen and R. Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutionalarchitecture. In In Proc. Int. Conf. Computer Vision (ICCV), 2015. 2, 3 [3]D. Eigen, C. Puhrsch, and R. Fergus. Prediction from a single image using a multi-sc...
通过深度卷积神经网络(Deep Convolutional Neural Networks, DCNNs)将图像转换为模型的参数。然而,以上所有的方法要么使用全连接层,要么在参数化的展开UV空间上使用二维卷积,从而得到具有许多参数的大型网络。在本文中,我们提出了第一个非线性3DMMs,通过使用直接网格(direct mesh)卷积学习联合纹理和形状自动编码器。我们...
论文笔记——Multi-Scale Dense Convolutional Networks for Efficient Prediction,程序员大本营,技术文章内容聚合第一站。
layer (convolutional kernel, 2 × 2 × 2; step size, 2 × 2 × 2) allowed to reduce the number of the network parameters, minimize overfitting, and reduce the model’s complexity. On the other hand, the transition layer solved the problem of changing the number of ...
To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery.This is a preview of subscription content, access via your ...
To solve this problem, we propose a deep learning framework that integrates a convolutional neural network and a capsule network. DenseCapsNet, a new deep learning framework, is formed by the fusion of a dense convolutional network (DenseNet) and the capsule neural network (CapsNet), leveraging ...
This study uses a convolutional neural network (CNN) model to combine the benefits of dense and residual blocks. The objective is to leverage the benefits of residual and dense connections to enhance information flow, gradient propagation, and feature reuse, ultimately improving the model's ...