In recent years, quite a few researchers have studied remote sensing image classification using CNNs, and CNNs can be applied to realize rapid, economical and accurate analysis and feature extraction from remote sensing data. This paper aims to provide a survey of the current state-of-the-art...
With the increasing complexity of image classification tasks, traditional convolutional neural networks face performance bottlenecks when dealing with intricate network structures. To address this issue, this paper proposes an image classification model based on a hybrid quantum-classical neural network. This...
With the increase of biological images, how to classify them effectively is a challenging problem, the Convolutional Neural Networks (CNNs) show promise for this problem. The challenges of using CNNs to handle images classification lie in two aspects: (1) How to further improve the classification...
Remote Sensing Scene Classification Based on Improved GhostNet Nowadays, the design of convolutional neural network (CNN) models is getting deeper and wider. When traditional CNN is used to process limited data of remote sensing images, it will lead to overfitting. We will use lightweight and effi...
Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain. In IEEE J. Biomedical and Health Informatics; 2017. p. 41-47. Google Scholar 19 Chen PJMCLMJLJCLHHSLaVST Accurate classification of diminutive colorectal polyps using computer-...
This paper presents a deep Convolutional Neural Network (CNN) based approach for document image classification. One of the main requirement of deep CNN arc... MZ Afzal,S Capobianco,MI Malik,... - International Conference on Document Analysis & Recognition 被引量: 19发表: 2015年 GPU-based Acce...
读《ImageNet Classification with Deep Convolutional Neural Networks》 bell arXiv综述论文“Image Segmentation Using Deep Learning” 以前在CSDN写的。 arXiv于2020年1月15日上传图像分割综述论文“Image Segmentation Using Deep Learning: A Survey“。 CSDN-专业IT技术社区-登录本文探讨的 网络模型包括:1)全卷积...
For simplicity, the terminology of TL in the remainder of the paper refers to homogeneous TL (i.e. both domains are image analysis) with pretrained CNN models using ImageNet data for medical image classification in a supervisory manner. Roughly, there are two TL approaches to leveraging CNN ...
In this paper, we propose a simple, efficient, and effective method using CNN activation features applied to classification and segmentation of histopathology images. From the experiments, our framework achieves good performance in two dataset. The advantages of our framework include: ...
1. A CNN with the same architecture as AlexNet [53] is used as base-model; 2. The classification layer is changed in order to have two classes as output: authentic or forged; 3. The weights of the AlexNet model trained on the ImageNet dataset [20] are used as initial weights fo...