Segmentation Technology of Nucleus Image Based on U-Net Network

To solve the problems of rough edge and poor segmentation accuracy of traditional neural networks in small nucleus image segmentation, a nucleus image segmentation technology based on U-Net network is proposed. First, the U-Net network is used to segment the nucleus image, which stitches the feature images in the channel dimension to achieve feature fusion, and the skip structure is used to combine the low- and high-level features. Then, the subregional average pooling is proposed to improve the global average pooling in the attention module, and an attention channel expansion module is designed to improve the accuracy of image segmentation. Finally, the improved attention module is integrated into the U-Net network to achieve accurate segmentation of the nuclear image. Based on the Python platform, the experimental results show that the proposed segmentation technology can achieve fast convergence, and the mean intersection over union (MIoU) is 85.02%, which is better than other comparison technologies and has a good application prospect.

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DCNN-based Ship Classification using Enhanced Edge Information and Inception Module

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2022.66.3.030501 ◽

2021 ◽

Author(s):

Bo Wang ◽

Xiaoting Yu ◽

Chengeng Huang ◽

Qinghong Sheng ◽

Yuanyuan Wang ◽

...

Keyword(s):

Neural Networks ◽

Classification Performance ◽

Image Features ◽

Deep Convolutional Neural Networks ◽

Edge Information ◽

Average Accuracy ◽

Ship Classification ◽

Edge Features ◽

High Level ◽

Better Than

The excellent feature extraction ability of deep convolutional neural networks (DCNNs) has been demonstrated in many image processing tasks, by which image classification can achieve high accuracy with only raw input images. However, the specific image features that influence the classification results are not readily determinable and what lies behind the predictions is unclear. This study proposes a method combining the Sobel and Canny operators and an Inception module for ship classification. The Sobel and Canny operators obtain enhanced edge features from the input images. A convolutional layer is replaced with the Inception module, which can automatically select the proper convolution kernel for ship objects in different image regions. The principle is that the high-level features abstracted by the DCNN, and the features obtained by multi-convolution concatenation of the Inception module must ultimately derive from the edge information of the preprocessing input images. This indicates that the classification results are based on the input edge features, which indirectly interpret the classification results to some extent. Experimental results show that the combination of the edge features and the Inception module improves DCNN ship classification performance. The original model with the raw dataset has an average accuracy of 88.72%, while when using enhanced edge features as input, it achieves the best performance of 90.54% among all models. The model that replaces the fifth convolutional layer with the Inception module has the best performance of 89.50%. It performs close to VGG-16 on the raw dataset and is significantly better than other deep neural networks. The results validate the functionality and feasibility of the idea posited.

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Prediction by using Artificial Neural Networks and Box-Jenkins methodologies: Comparison Study

Journal of Al-Qadisiyah for Computer Science and Mathematics ◽

10.29304/jqcm.2017.9.2.325 ◽

2017 ◽

Vol 9 (2) ◽

Author(s):

Mohammed Habib Al- Sharoot ◽

Emaan Yousif Abdoon

Keyword(s):

Neural Networks ◽

Exchange Rate ◽

Absolute Error ◽

Percentage Error ◽

Neural Network Approach ◽

Decision Making Processes ◽

The Mean ◽

Us Dollar ◽

Stable Economy ◽

Better Than

The variations in exchange rate, especially the sudden unexpected increases and decreases, have significant impact on the national economy of any country. Iraq is no exception; therefore, the accurate forecasting of exchange rate of Iraqi dinar to US dollar plays an important role in the planning and decision-making processes as well as the maintenance of a stable economy in Iraq. This research aims to compare Box-Jenkins methodology to neural networks in terms of forecasting the exchange rate of Iraqi dinar to US dollar based on data provided by the Iraqi Central Bank for the period 30/01/2004 and 30/12/2014. Based on the Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE) as criteria to compare the two methodologies, it was concluded that Box-Jenkins is better than neural network approach in forecasting.

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Top-Down Pyramid Fusion Network for High-Resolution Remote Sensing Semantic Segmentation

Remote Sensing ◽

10.3390/rs13204159 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4159

Author(s):

Yuhang Gu ◽

Jie Hao ◽

Bing Chen ◽

Hai Deng

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Feature Fusion ◽

Semantic Segmentation ◽

Semantic Knowledge ◽

Surface Model ◽

Top Down ◽

Segmentation Accuracy ◽

Fusion Methods ◽

High Level

In recent years, high-resolution remote sensing semantic segmentation based on data fusion has gradually become a research focus in the field of land classification, which is an indispensable task of a smart city. However, the existing feature fusion methods with bottom-up structures can achieve limited fusion results. Alternatively, various auxiliary fusion modules significantly increase the complexity of the models and make the training process intolerably expensive. In this paper, we propose a new lightweight model called top-down pyramid fusion network (TdPFNet) including a multi-source feature extractor, a top-down pyramid fusion module and a decoder. It can deeply fuse features from different sources in a top-down structure using high-level semantic knowledge guiding the fusion of low-level texture information. Digital surface model (DSM) data and open street map (OSM) data are used as auxiliary inputs to the Potsdam dataset for the proposed model evaluation. Experimental results show that the network proposed in this paper not only notably improves the segmentation accuracy, but also reduces the complexity of the multi-source semantic segmentation model.

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Mathematical Modelling of Ground Truth Image for 3D Microscopic Objects Using Cascade of Convolutional Neural Networks Optimized with Parameters’ Combinations Generators

Symmetry ◽

10.3390/sym12030416 ◽

2020 ◽

Vol 12 (3) ◽

pp. 416

Author(s):

Omar Bilalovic ◽

Zikrija Avdagic ◽

Samir Omanovic ◽

Ingmar Besic ◽

Vedad Letic ◽

...

Keyword(s):

Neural Networks ◽

Image Segmentation ◽

Mathematical Modelling ◽

Convolutional Neural Networks ◽

Data Augmentation ◽

Ground Truth ◽

3D Images ◽

Segmentation Accuracy ◽

Augmentation Techniques ◽

Ground Truth Image

Mathematical modelling to compute ground truth from 3D images is an area of research that can strongly benefit from machine learning methods. Deep neural networks (DNNs) are state-of-the-art methods design for solving these kinds of difficulties. Convolutional neural networks (CNNs), as one class of DNNs, can overcome special requirements of quantitative analysis especially when image segmentation is needed. This article presents a system that uses a cascade of CNNs with symmetric blocks of layers in chain, dedicated to 3D image segmentation from microscopic images of 3D nuclei. The system is designed through eight experiments that differ in following aspects: number of training slices and 3D samples for training, usage of pre-trained CNNs and number of slices and 3D samples for validation. CNNs parameters are optimized using linear, brute force, and random combinatorics, followed by voter and median operations. Data augmentation techniques such as reflection, translation and rotation are used in order to produce sufficient training set for CNNs. Optimal CNN parameters are reached by defining 11 standard and two proposed metrics. Finally, benchmarking demonstrates that CNNs improve segmentation accuracy, reliability and increased annotation accuracy, confirming the relevance of CNNs to generate high-throughput mathematical ground truth 3D images.

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A Prototype Model for Semantic Segmentation of Curvilinear Meandering Regions by Deconvolutional Neural Networks

Applied Computer Systems ◽

10.2478/acss-2020-0008 ◽

2020 ◽

Vol 25 (1) ◽

pp. 62-69

Author(s):

Vadim Romanuke

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Image Segmentation ◽

Region Of Interest ◽

Semantic Segmentation ◽

Regions Of Interest ◽

The Other ◽

Prototype Model ◽

White Background ◽

The Mean

AbstractDeconvolutional neural networks are a very accurate tool for semantic image segmentation. Segmenting curvilinear meandering regions is a typical task in computer vision applied to navigational, civil engineering, and defence problems. In the study, such regions of interest are modelled as meandering transparent stripes whose width is not constant. The stripe on the white background is formed by the upper and lower non-parallel black curves so that the upper and lower image parts are completely separated. An algorithm of generating datasets of such regions is developed. It is revealed that deeper networks segment the regions more accurately. However, the segmentation is harder when the regions become bigger. This is why an alternative method of the region segmentation consisting in segmenting the upper and lower image parts by subsequently unifying the results is not effective. If the region of interest becomes bigger, it must be squeezed in order to avoid segmenting the empty image. Once the squeezed region is segmented, the image is conversely rescaled to the original view. To control the accuracy, the mean BF score having the least value among the other accuracy indicators should be maximised first.

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DFT Benchmark Study of the O--O Bond Dissociation Energy in Peroxides Validated with High-Level Ab-Initio Calculations

10.26434/chemrxiv.8217221.v1 ◽

2019 ◽

Author(s):

Danilo Carmona ◽

Pablo Jaque ◽

Esteban Vöhringer-Martinez

Keyword(s):

Reference Value ◽

Basis Set ◽

Basis Sets ◽

Mean Deviation ◽

Bond Dissociation ◽

Dissociation Energies ◽

The Mean ◽

Experimental Values ◽

High Level ◽

Almost All

<div><div><div><p>Peroxides play a central role in many chemical and biological pro- cesses such as the Fenton reaction. The relevance of these compounds lies in the low stability of the O–O bond which upon dissociation results in radical species able to initiate various chemical or biological processes. In this work, a set of 64 DFT functional-basis set combinations has been validated in terms of their capability to describe bond dissociation energies (BDE) for the O–O bond in a database of 14 ROOH peroxides for which experimental values ofBDE are available. Moreover, the electronic contributions to the BDE were obtained for four of the peroxides and the anion H2O2− at the CBS limit at CCSD(T) level with Dunning’s basis sets up to triple–ζ quality provid- ing a reference value for the hydrogen peroxide anion as a model. Almost all the functionals considered here yielded mean absolute deviations around 5.0 kcal mol−1. The smallest values were observed for the ωB97 family and the Minnesota M11 functional with a marked basis set dependence. Despite the mean deviation, order relations among BDE experimental values of peroxides were also considered. The ωB97 family was able to reproduce the relations correctly whereas other functionals presented a marked dependence on the chemical nature of the R group. Interestingly, M11 functional did not show a very good agreement with the established order despite its good performance in the mean error. The obtained results support the use of similar validation strategies for proper prediction of BDE or other molecular properties by DF Tmethods in subsequent related studies.</p></div></div></div>

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The way of value of Correlation of genomic DNA microsatellite loci and live weight of Chukchi reindeer

Genetika i razvedenie zhivotnyh ◽

10.31043/2410-2733-2020-3-27-32 ◽

2020 ◽

pp. 27-32

Author(s):

V. Dodokhov ◽

N. Pavlova ◽

T. Rumyantseva ◽

L. Kalashnikova

Keyword(s):

Microsatellite Markers ◽

Agricultural Production ◽

Live Weight ◽

Biodiversity Loss ◽

Tribal Community ◽

High Germination ◽

Wide Range ◽

The Mean ◽

High Level ◽

Dna Microsatellite

The article presents the genetic characteristic of the Chukchi reindeer breed. The object of the study was of the Chukchi reindeer. In recent years, the number of reindeer of the Chukchi breed has declined sharply. Reduced reindeer numbers could lead to biodiversity loss. The Chukchi breed of deer has good meat qualities, has high germination viability and is adapted in adverse tundra conditions of Yakutia. Herding of the Chukchi breed of deer in Yakutia are engaged only in the Nizhnekolymsky district. There are four generic communities and the largest of which is the agricultural production cooperative of nomadic tribal community «Turvaurgin», which was chosen to assess the genetic processes of breed using microsatellite markers: Rt6, BMS1788, Rt 30, Rt1, Rt9, FCB193, Rt7, BMS745, C 143, Rt24, OheQ, C217, C32, NVHRT16, T40, C276. It was found that microsatellite markers have a wide range of alleles and generally have a high informative value for identifying of genetic differences between animals and groups of animal. The number of identified alleles is one of the indicators of the genetic diversity of the population. The total number of detected alleles was 127. The Chukchi breed of deer is characterized by a high level of heterozygosity, and the random crossing system prevails over inbreeding in the population. On average, there were 7.9 alleles (Na) per locus, and the mean number of effective alleles (Ne) was 4.1. The index of fixation averaged 0.001. The polymorphism index (PIC) ranged from 0.217 to 0.946, with an average of 0.695.

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Visible Laser Probing (VLP) with GaP Solid Immersion Lens Demonstrating 110 nm Resolution in Common Laser Probing Applications

ISTFA 2016: Conference Proceedings from the 42nd International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2016p0027 ◽

2016 ◽

Author(s):

Travis Eiles ◽

Patrick Pardy

Keyword(s):

Gallium Phosphide ◽

Fault Isolation ◽

Solid Immersion Lens ◽

Laser Probing ◽

Isolation Methods ◽

Visible Laser ◽

High Level ◽

Industry Needs ◽

Diameter Reduction ◽

Better Than

Abstract This paper demonstrates a breakthrough method of visible laser probing (VLP), including an optimized 577 nm laser microscope, visible-sensitive detector, and an ultimate-resolution gallium phosphide-based solid immersion lens on the 10 nm node, showing a 110 nm resolution. This is 2x better than what is achieved with the standard suite of probing systems using typical infrared (IR) wavelengths today. Since VLP provides a spot diameter reduction of 0.5x over IR methods, it is reasonable, based simply on geometry, to project that VLP using the 577 nm laser will meet the industry needs for laser probing for both the 10 nm and 7 nm process nodes. Based on its high level of optimization, including high resolution and specialized solid immersion lens, it is highly likely that this VLP technology will be one of the last optically-based fault isolation methods successfully used.

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