An Efficient Method of Histological Cell Image Detection Based on Spatial Information Convolution Neural Network

2019 ◽  
Author(s):  
Qi Qiang ◽  
Wang Hong ◽  
Peng Likang
Keyword(s):  
Neural Network ◽  
Efficient Method ◽  
Spatial Information ◽  
Convolution Neural Network ◽  
Image Detection ◽  
Cell Image

scholarly journals ODPA-CNN: One Dimensional Parallel Atrous Convolution Neural Network for Band-Selective Hyperspectral Image Classification

2021 ◽  
Vol 12 (1) ◽  
pp. 174
Author(s):  
Byungjin Kang ◽  
Inho Park ◽  
Changmin Ok ◽  
Sungho Kim
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Convolution Neural Network ◽  
Spectral Information ◽  
Hyperspectral Image Classification ◽  
One Dimensional ◽  
Proposed Model ◽  
3D Cnn ◽  
2D And 3D

Recently, hyperspectral image (HSI) classification using deep learning has been actively studied using 2D and 3D convolution neural networks (CNN). However, they learn spatial information as well as spectral information. These methods can increase the accuracy of classification, but do not only focus on the spectral information, which is a big advantage of HSI. In addition, the 1D-CNN, which learns only pure spectral information, has limitations because it uses adjacent spectral information. In this paper, we propose a One Dimensional Parellel Atrous Convolution Neural Network (ODPA-CNN) that learns not only adjacent spectral information for HSI classification, but also spectral information from a certain distance. It extracts features in parallel to account for bands of varying distances. The proposed method excludes spatial information such as the shape of an object and performs HSI classification only with spectral information about the material of the object. Atrous convolution is not a convolution of adjacent spectral information, but a convolution between spectral information separated by a certain distance. We compare the proposed model with various datasets to the other models. We also test with the data we have taken ourselves. Experimental results show a higher performance than some 3D-CNN models and other 1D-CNN methods. In addition, using datasets to which random space is applied, the vulnerabilities of 3D-CNN are identified, and the proposed model is shown to be robust to datasets with little spatial information.

scholarly journals Convolution Neural Network Based on Two-Dimensional Spectrum for Hyperspectral Image Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hongmin Gao ◽  
Shuo Lin ◽  
Yao Yang ◽  
Chenming Li ◽  
Mingxiang Yang
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Convolution Neural Network ◽  
Image Method ◽  
Two Dimensional ◽  
Input Unit ◽  
Dimensional Spectrum ◽  
Kennedy Space

Inherent spectral characteristics of hyperspectral image (HSI) data are determined and need to be deeply mined. A convolution neural network (CNN) model of two-dimensional spectrum (2D spectrum) is proposed based on the advantages of deep learning to extract feature and classify HSI. First of all, the traditional data processing methods which use small area pixel block or one-dimensional spectral vector as input unit bring many heterogeneous noises. The 2D-spectrum image method is proposed to solve the problem and make full use of spectral value and spatial information. Furthermore, a batch normalization algorithm (BN) is introduced to address internal covariate shifts caused by changes in the distribution of input data and expedite the training of the network. Finally, Softmax loss models are proposed to induce competition among the outputs and improve the performance of the CNN model. The HSI datasets of experiments include Indian Pines, Salinas, Kennedy Space Center (KSC), and Botswana. Experimental results show that the overall accuracies of the 2D-spectrum CNN model can reach 98.26%, 97.28%, 96.22%, and 93.64%. These results are higher than the accuracies of other traditional methods described in this paper. The proposed model can achieve high target classification accuracy and efficiency.

Hyperspectral Image Classification Using Spatial and Edge Features Based on Deep Learning

2019 ◽  
Vol 33 (09) ◽  
pp. 1954027 ◽  
Author(s):  
Dexiang Zhang ◽  
Jingzhong Kang ◽  
Lina Xun ◽  
Yu Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Bilateral Filter ◽  
Convolution Neural Network ◽  
Hyperspectral Images ◽  
Data Sets ◽  
Edge Information

In recent years, deep learning has been widely used in the classification of hyperspectral images and good results have been achieved. But it is easy to ignore the edge information of the image when using the spatial features of hyperspectral images to carry out the classification experiments. In order to make full use of the advantages of convolution neural network (CNN), we extract the spatial information with the method of minimum noise fraction (MNF) and the edge information by bilateral filter. The combination of the two kinds of information not only increases the useful information but also effectively removes part of the noise. The convolution neural network is used to extract features and classify for hyperspectral images on the basis of this fused information. In addition, this paper also uses another kind of edge-filtering method to amend the final classification results for a better accuracy. The proposed method was tested on three public available data sets: the University of Pavia, the Salinas, and the Indian Pines. The competitive results indicate that our approach can realize a classification of different ground targets with a very high accuracy.

scholarly journals SSCNN-S: A Spectral-Spatial Convolution Neural Network with Siamese Architecture for Change Detection

2021 ◽  
Vol 13 (5) ◽  
pp. 895
Author(s):  
Tianming Zhan ◽  
Bo Song ◽  
Yang Xu ◽  
Minghua Wan ◽  
Xin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Change Detection ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Real Data ◽  
Mean Value ◽  
Convolution Neural Network ◽  
Scoring Method ◽  
Siamese Network ◽  
The Impact

In this paper, a spectral-spatial convolution neural network with Siamese architecture (SSCNN-S) for hyperspectral image (HSI) change detection (CD) is proposed. First, tensors are extracted in two HSIs recorded at different time points separately and tensor pairs are constructed. The tensor pairs are then incorporated into the spectral-spatial network to obtain two spectral-spatial vectors. Thereafter, the Euclidean distances of the two spectral-spatial vectors are calculated to represent the similarity of the tensor pairs. We use a Siamese network based on contrastive loss to train and optimize the network so that the Euclidean distance output by the network describes the similarity of tensor pairs as accurately as possible. Finally, the values obtained by inputting all tensor pairs into the trained model are used to judge whether a pixel belongs to the change area. SSCNN-S aims to transform the problem of HSI CD into a problem of similarity measurement for tensor pairs by introducing the Siamese network. The network used to extract tensor features in SSCNN-S combines spectral and spatial information to reduce the impact of noise on CD. Additionally, a useful four-test scoring method is proposed to improve the experimental efficiency instead of taking the mean value from multiple measurements. Experiments on real data sets have demonstrated the validity of the SSCNN-S method.

The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

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