scholarly journals Application of Big Data Deep Learning in Auxiliary Diagnosis of Lower Extremity Arteriosclerosis Obliterans

2021 ◽  
Vol 5 (5) ◽  
pp. 108-112
Author(s):  
Linbo Liu ◽  
Yang Liu ◽  
Hongjun Wang ◽  
Yi Zhang ◽  
Zhijie Liao ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Lower Limb ◽  
Image Data ◽  
Lower Extremities ◽  
Lifestyle Changes ◽  
Learning Technology ◽  
Imaging Data ◽  
Arteriosclerosis Obliterans ◽  
Medical Institutions

At present, the incidence rate of arteriosclerosis obliterans (LEASO) of the lower extremities is significantly increased by aging and lifestyle changes. It is of great importance to predict the LEASO effectively and accurately by analyzing the imaging data of the lower extremities [1]. At this stage, China has entered the era of big data and artificial intelligence. Medical institutions at all levels can produce a large number of lower limb vascular image data every day. Using big data deep learning technology to intelligently analyze a large number of image data, and then carry out auxiliary diagnosis, so as to improve the diagnosis and treatment effect of LEASO is the focus of clinical research.

scholarly journals A Systematic Analysis of Big Image Data Methodologies in Various Applications

2020 ◽  
Vol 9 (5) ◽  
pp. 483-487
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Scale ◽  
Image Data ◽  
Computational Time ◽  
Process Data ◽  
Systematic Analysis ◽  
Large Scale Data ◽  
Learning Techniques ◽  
Effective Performance

Big data is large-scale data collected for knowledge discovery, it has been widely used in various applications. Big data often has image data from the various applications and requires effective technique to process data. In this paper, survey has been done in the big image data researches to analysis the effective performance of the methods. Deep learning techniques provides the effective performance compared to other methods included wavelet based methods. The deep learning techniques has the problem of requiring more computational time, and this can be overcome by lightweight methods.

L2MXception: An Improved Xception Network for Classification of Peach diseases 

2020 ◽  
Author(s):  
Na Yao ◽  
Fuchuan Ni ◽  
Ziyan Wang ◽  
Jun Luo ◽  
Wing-Kin Sung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Identification ◽  
Image Data ◽  
Yield Reduction ◽  
Disease Prediction ◽  
Imaging Data ◽  
Regularization Term ◽  
Detection And Identification ◽  
L2 Norm

Abstract Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue.Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.

scholarly journals Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry

2021 ◽  
Vol 12 ◽  
Author(s):  
Xu Xiao ◽  
Ying Qiao ◽  
Yudi Jiao ◽  
Na Fu ◽  
Wenxian Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Single Cell ◽  
Image Data ◽  
Basic Research ◽  
Cell Segmentation ◽  
Imaging Method ◽  
Imaging Data ◽  
Mass Cytometry ◽  
Multiplexed Imaging

Highly multiplexed imaging technology is a powerful tool to facilitate understanding the composition and interactions of cells in tumor microenvironments at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 100 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge to its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results or require manual annotation, which is very time consuming. Here, we developed Dice-XMBD1, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used for IMC images, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane, and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

2018 ◽  
Vol 29 (3) ◽  
pp. 67-88 ◽  
Author(s):  
Wen Zeng ◽  
Hongjiao Xu ◽  
Hui Li ◽  
Xiang Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Correlation Analysis ◽  
Vector Space ◽  
Input Word ◽  
Learning Technology ◽  
Depth Analysis ◽  
Different Types ◽  
High Level ◽  
Deep Learning Model

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.

scholarly journals Automatic Generalization of Residential Areas Based on “Paradigm” Theory and Big Data

2021 ◽  
Author(s):  
Tianlin Duo ◽  
Peng Zhang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Scale ◽  
Information Science ◽  
Residential Area ◽  
Learning Technology ◽  
Residential Areas ◽  
Area Selection ◽  
Cartographic Generalization ◽  
Map Data

“Paradigm” theory is an important ideological and practical tool for scientific research. The research means and methods of Geographic Information Science follow the laws of four paradigms. Automatic cartographic generalization is not only the key link of map making, but also a recognized difficult and hot issue. Based on large-scale map data and deep learning technology, an automatic cartographic generalization problem-solving model is proposed in this paper. According to the key and difficult problems faced by residential area selection and simplification, residential area selection models and simplification models based on big data and deep learning are constructed respectively, which provides new ideas and schemes to solve the key and difficult problems of residential area selection and simplification.

APPLICATION OF DEEP LEARNING IN MEDICAL IMAGING

2020 ◽  
Vol 03 (04) ◽  
pp. 7-13
Author(s):  
Elcin Nizami Huseyn ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Medical Imaging ◽  
Key Words ◽  
Learning Technology ◽  
Application Research ◽  
Imaging Diagnosis ◽  
Experience Machine

Medical imaging technology plays an important role in the detection, diagnosis and treatment of diseases. Due to the instability of human expert experience, machine learning technology is expected to assist researchers and physicians to improve the accuracy of imaging diagnosis and reduce the imbalance of medical resources. This article systematically summarizes some methods of deep learning technology, introduces the application research of deep learning technology in medical imaging, and discusses the limitations of deep learning technology in medical imaging. Key words: Artificial Intelligence, Deep Learning, Medical Imaging, big data

Research on Methodology of Correlation Analysis of Sci-Tech Literature Based on Deep Learning Technology in the Big Data

2020 ◽  
pp. 1524-1546
Author(s):  
Wen Zeng ◽  
Hongjiao Xu ◽  
Hui Li ◽  
Xiang Li
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Correlation Analysis ◽  
Vector Space ◽  
Input Word ◽  
Learning Technology ◽  
Depth Analysis ◽  
Presentation Method ◽  
High Level ◽  
Deep Learning Model

In the big data era, it is a great challenge to identify high-level abstract features out of a flood of sci-tech literature to achieve in-depth analysis of data. The deep learning technology has developed rapidly and achieved applications in many fields, but has rarely been utilized in the research of sci-tech literature data. This article introduced the presentation method of vector space of terminologies in sci-tech literature based on the deep learning model. It explored and adopted a deep AE model to reduce the dimensionality of input word vector feature. Also put forward is the methodology of correlation analysis of sci-tech literature based on deep learning technology. The experimental results showed that the processing of sci-tech literature data could be simplified into the computation of vectors in the multi-dimensional vector space, and the similarity in vector space could be used to represent similarity in text semantics. The correlation analysis of subject contents between sci-tech literatures of the same or different types can be made using this method.

scholarly journals Toward Efficient Image Recognition in Sensor-Based IoT: A Weight Initialization Optimizing Method for CNN Based on RGB Influence Proportion

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2866 ◽  
Author(s):  
Zile Deng ◽  
Yuanlong Cao ◽  
Xinyu Zhou ◽  
Yugen Yi ◽  
Yirui Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Image Data ◽  
Human Vision ◽  
Learning Technology ◽  
Complex Data ◽  
Training Process ◽  
Color Differences ◽  
Weight Initialization ◽  
The Internet Of Things

As the Internet of Things (IoT) is predicted to deal with different problems based on big data, its applications have become increasingly dependent on visual data and deep learning technology, and it is a big challenge to find a suitable method for IoT systems to analyze image data. Traditional deep learning methods have never explicitly taken the color differences of data into account, but from the experience of human vision, colors play differently significant roles in recognizing things. This paper proposes a weight initialization method for deep learning in image recognition problems based on RGB influence proportion, aiming to improve the training process of the learning algorithms. In this paper, we try to extract the RGB proportion and utilize it in the weight initialization process. We conduct several experiments on different datasets to evaluate the effectiveness of our proposal, and it is proven to be effective on small datasets. In addition, as for the access to the RGB influence proportion, we also provide an expedient approach to get the early proportion for the following usage. We assume that the proposed method can be used for IoT sensors to securely analyze complex data in the future.

Sign in / Sign up

Export Citation Format

Share Document