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...
除此之外,DCNN需要大量的带有人工标注信息的训练数据,这会耗费很多人力物力,甚至在某些领域根本无法获得数据量很大的数据集。为了解决这些问题,此文[1]结合了扩张卷积 (dilated convolution) 和密集连接 (dense connection),提出了Mixed-Scale Dense Convolutional Network (MS-D网络),MS-D网络和传统的DCNN相比,提高了...
In spite of the fact that convolutional neural network-based stereo matching models have shown good performance in both accuracy and robustness, the issue of image feature loss in regions of texture-less, complex scenes and occlusions remains. In this paper, we present a dense convolutional neural...
In this paper, we presented a novel MR image SR method based on a dense convolutional neural network (DDSR), and its enhanced version called EDDSR. There are three major innovations: first, we re-designed dense modules to extract hierarchical features directly from LR images and propagate the...
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...
Traditional Convolutional Neural Network (CNN)-based methods, Transformer-based methods, and hybrid approaches combining these two struggle to balance performance and model complexity. To achieve efficient predictions with a low parameter count, we propose DenseAttentionNetwork (DANet), a lightweight ...
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...
This paper proposes a new deep convolutional neural network (DCNN) architecturethat learns pixel embeddings, such that pairwise distances between the embeddingscan be used to infer whether or not the pixels lie on the same region.That is, for any two pixels on the same object, the embeddings ...
Fully convolutional network TOF: Time of flight CNN: Convolutional neural network CTA: Computed tomography angiography MIP: Maximal intensity projection 2D: Two-dimensional DSA: Digital subtraction angiography CBAM: Convolutional block attention module MFB: Multiscale fusion block GN: Group ...
[19]F. Liu, C. Shen, and G. Lin. Deep convolutional neural fields for depth estimation from a single image. In Proc. Conf. Computer Vision and Pattern Recognition (CVPR), pages 5162–5170, 2015. 3 [20]R. Mur-Artal, J. M. M. Montiel, and J. D. Tards. Orb-slam: A versati...