Deep learning of DEM image texture for landform classification in the Shandong area, China

2021 ◽  
Author(s):  
Yuexue Xu ◽  
Hongchun Zhu ◽  
Changyu Hu ◽  
Haiying Liu ◽  
Yu Cheng
Keyword(s):  
Deep Learning ◽  
Image Texture ◽  
Landform Classification

P3430Classification of pronounced subclinical atherosclerosis: deep learning approach of carotid intima media ultrasound images is superior to clinical risk factors

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
E Nyman ◽  
M Karlsson ◽  
U Naslund ◽  
C Gronlund
Keyword(s):  
Risk Factors ◽  
Deep Learning ◽  
Subclinical Atherosclerosis ◽  
Ultrasound Image ◽  
Area Under The Curve ◽  
Image Texture ◽  
Learning Approach ◽  
Data Set ◽  
Vascular Age ◽  
Traditional Risk Factors

Abstract Background Carotid ultrasound measurements of subclinical atherosclerosis is extensively used in the research field of cardiovascular disease. Increased intima media thickness (IMT) and plaque detection have predictive value for cardiovascular events when added to traditional risk factors. However, among studies different protocols for measuring IMT (projections, mean or max values and sites) are used and methodological difficulties of plaque detection, together result in conflicting results. Recently, Deep Learning image driven classification methods, has been successfully applied in several medical imaging applications. Here we hypothesize that ultrasound image texture of the intima media complex accurately reflects the disease burden without the need to measure IMT values or detect plaques. Purpose To evaluate classification accuracy of ultrasound based deep learning approach of the intima media complex image compared to traditional risk factors for participants with no vs pronounced subclinical atherosclerosis. Methods Subjects from the VIPVIZA study (Visualization of asymptomatic atherosclerotic disease for optimum cardiovascular prevention, n: 3532, 40, 50 and 60 year old, 53% women) were selected for analysis. Bilateral carotid ultrasound examinations were performed according to a standardized protocol. Subjects were categorized in two groups as 1) pronounced subclinical atherosclerosis (n: 401) – bilateral plaques and estimated vascular age 10 years older, or 2) No subclinical atherosclerosis (n: 592) – no plaques and estimated ordinary vascular age. Traditional risk factors for the participants were estimated by the SCORE risk chart. A 1-cm wide region of the distal common carotid artery intima media complex was automatically segmented from the original B-mode images. The images were fed to a Deep Learning model, convolution neural network (CNN), trained using transfer learning model with 60% training data set and 40% evaluation data set. Classification performance was quantified using accuracy of ROC analysis. Results The mean age was 58 and 56 years in groups 1 and 2, respectively (with 43% and 56% women, respectively). The mean SCORE was 1.74 in group 1 and 1.09 in group 2. Classification based on SCORE had an area under the curve of 0.69 with an accuracy of 38%. The Deep learning approach had an area under the curve of 0.89 with an accuracy of 78%. Intima media image based classification Conclusion The results shows that ultrasound image texture of the intima media with Deep Learning approach can be used to detect pronounced disease without explicit measurement of IMT values or detection of plaques. With hard end-points, the approach could be used for risk stratification of subclinical atherosclerosis. Acknowledgement/Funding Västerbotten County Council, Swedish Research Council, Heart and Lung Foundation, Carl Bennet Ltd, Sweden.

scholarly journals MalDeep: A Deep Learning Classification Framework against Malware Variants Based on Texture Visualization

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Yuntao Zhao ◽  
Chunyu Xu ◽  
Bo Bo ◽  
Yongxin Feng
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Feature Space ◽  
Image Texture ◽  
Accuracy Rate ◽  
Texture Representation ◽  
Malware Classification ◽  
Classification Framework ◽  
Average Accuracy ◽  
New Feature

The increasing sophistication of malware variants such as encryption, polymorphism, and obfuscation calls for the new detection and classification technology. In this paper, MalDeep, a novel malware classification framework of deep learning based on texture visualization, is proposed against malicious variants. Through code mapping, texture partitioning, and texture extracting, we can study malware classification in a new feature space of image texture representation without decryption and disassembly. Furthermore, we built a malware classifier on convolutional neural network with two convolutional layers, two downsampling layers, and many full connection layers. We adopt the dataset, from Microsoft Malware Classification Challenge including 9 categories of malware families and 10868 variant samples, to train the model. The experiment results show that the established MalDeep has a higher accuracy rate for malware classification. In particular, for some backdoor families, the classification accuracy of the model reaches over 99%. Moreover, compared with other main antivirus software, MalDeep also outperforms others in the average accuracy for the variants from different families.

Deep learning-based approach for landform classification from integrated data sources of digital elevation model and imagery

2020 ◽  
Author(s):  
Sijin Li ◽  
Liyang Xiong ◽  
Guoan Tang ◽  
Josef Strobl
Keyword(s):  
Deep Learning ◽  
Loess Plateau ◽  
Data Sources ◽  
The Loess Plateau ◽  
Transitional Area ◽  
Landform Classification ◽  
The Earth ◽  
Digital Elevation ◽  
Elevation Model ◽  
External Forces

<p>Landform classification is one of the most important aspects in geomorphological research, dividing the Earth’s surface into diverse geomorphological types. Thus, an accurate classification of landforms is a key procedure in describing the topographic characteristics of a given area and understanding their inner geomorphological formation processes. However, landform types are not always independent of one another due to the complexity and dynamics of interior and external forces. Furthermore, transitional landforms with gradually changing surface morphologies are widely distributed on the Earth’s surface. With this situation, classifying these complex and transitional landforms with traditional landform classification methods is hard. In this study, a deep learning (DL) algorithm was introduced, aiming at automatically classifying complex and transitional landforms. This algorithm was trained to learn and extract landform features from integrated data sources. These integrated data sources contain different combinations of imagery, digital elevation models (DEMs), and terrain derivatives. The Loess Plateau in China, which contains complex and transitional loess landforms, was selected as the study area for data training. In addition, two sample areas in the Loess Plateau with complex and transitional loess hill and ridge landforms were used to validate the classified landform types by using the proposed DL method. Meanwhile, a comparative analysis between the proposed DL and random forest (RF) methods was also conducted to investigate their capabilities in landform classification. The proposed DL approach can achieve the highest landform classification accuracy of 87% in the transitional area with data combination of DEMs and images. In addition, the proposed DL method can achieve a higher accuracy of landform classification with better defined landform boundaries compared to the RF method. The classified loess landforms indicate the different landform development stages in this area. Finally, the proposed DL method can be extended to other landform areas for classifying their complex and transitional landforms.</p>

scholarly journals Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomontages

2021 ◽  
Vol 11 (4) ◽  
pp. 1754
Author(s):  
Jooyoung Kim ◽  
Sojung Go ◽  
Kyoungjin Noh ◽  
Sangjun Park ◽  
Soochahn Lee
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Experimental Evaluation ◽  
Image Texture ◽  
Peripheral Retina ◽  
Image Artifacts ◽  
Retinal Diseases ◽  
Fundus Images ◽  
Learning Methods ◽  
Retinal Fundus

Retinal photomontages, which are constructed by aligning and integrating multiple fundus images, are useful in diagnosing retinal diseases affecting peripheral retina. We present a novel framework for constructing retinal photomontages that fully leverage recent deep learning methods. Deep learning based object detection is used to define the order of image registration and blending. Deep learning based vessel segmentation is used to enhance image texture to improve registration performance within a two step image registration framework comprising rigid and non-rigid registration. Experimental evaluation demonstrates the robustness of our montage construction method with an increased amount of successfully integrated images as well as reduction of image artifacts.

Sign in / Sign up

Export Citation Format

Share Document