scholarly journals Using Convolutional Networks and Satellite Imagery to Predict Disease Density in a Developing Country

2019 ◽  
Author(s):  
Rahman Sanya ◽  
Gilbert Maiga ◽  
Ernest Mwebaze
Keyword(s):  
Infectious Diseases ◽  
Developing Country ◽  
Satellite Imagery ◽  
Spatial Dynamics ◽  
Image Data ◽  
Urban Housing ◽  
Digital Data ◽  
Data Set ◽  
Convolutional Networks ◽  
Multiple Data

Rapid increase in digital data coupled with advances in deep learning algorithms is opening unprecedented opportunities for incorporating multiple data sources for modeling spatial dynamics of human infectious diseases. We used Convolutional Neural Networks (CNN) in conjunction with satellite imagery-based urban housing and socio-economic data to predict disease density in a developing country setting. We explored both single (uni) and multiple input (multimodality) network architectures for this purpose. We achieved maximum test set accuracy of 81.6 per cent using a single input CNN model built with one convolutional layer and trained using housing image data. However, this fairly good performance was biased in favor of specific disease density classes due to an unbalanced data set despite our use of methods to address the problem. These results suggest CNN are promising for modeling spatial dynamics of human infectious diseases, especially in a developing country setting. Urban housing signals extracted from satellite imagery seem suitable for this purpose, under the same context.

scholarly journals Using Deep Learning for Image-Based Potato Tuber Disease Detection

2019 ◽  
Vol 109 (6) ◽  
pp. 1083-1087 ◽  
Author(s):  
Dor Oppenheim ◽  
Guy Shani ◽  
Orly Erlich ◽  
Leah Tsror
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Image Data ◽  
Object Identification ◽  
Plant Diseases ◽  
Disease Classification ◽  
Successful Implementation ◽  
Potato Tubers ◽  
Data Set ◽  
Convolutional Networks

Many plant diseases have distinct visual symptoms, which can be used to identify and classify them correctly. This article presents a potato disease classification algorithm that leverages these distinct appearances and advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network, training it to classify the tubers into five classes: namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes, and diseases, was acquired, classified, and labeled manually by experts. The models were trained over different train-test splits to better understand the amount of image data needed to apply deep learning for such classification tasks. The models were tested over a data set of images taken using standard low-cost RGB (red, green, and blue) sensors and were tagged by experts, demonstrating high classification accuracy. This is the first article to report the successful implementation of deep convolutional networks, popular in object identification, to the task of disease identification in potato tubers, showing the potential of deep learning techniques in agricultural tasks.

scholarly journals Toward a Lifecycle Information Framework and Technology in Manufacturing

2017 ◽  
Vol 17 (2) ◽  
Author(s):  
Thomas Hedberg ◽  
Allison Barnard Feeney ◽  
Moneer Helu ◽  
Jaime A. Camelio
Keyword(s):  
Information Modeling ◽  
Digital Data ◽  
Use Case ◽  
Product Lifecycle ◽  
Dynamic Information ◽  
Data Set ◽  
Data Repositories ◽  
Domain Specific ◽  
Multiple Data ◽  
Information Framework

Industry has been chasing the dream of integrating and linking data across the product lifecycle and enterprises for decades. However, industry has been challenged by the fact that the context in which data are used varies based on the function/role in the product lifecycle that is interacting with the data. Holistically, the data across the product lifecycle must be considered an unstructured data set because multiple data repositories and domain-specific schema exist in each phase of the lifecycle. This paper explores a concept called the lifecycle information framework and technology (LIFT). LIFT is a conceptual framework for lifecycle information management and the integration of emerging and existing technologies, which together form the basis of a research agenda for dynamic information modeling in support of digital-data curation and reuse in manufacturing. This paper provides a discussion of the existing technologies and activities that the LIFT concept leverages. Also, the paper describes the motivation for applying such work to the domain of manufacturing. Then, the LIFT concept is discussed in detail, while underlying technologies are further examined and a use case is detailed. Lastly, potential impacts are explored.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

Maintenance of a Genetic Data Set on Multiple Data Base and Statistical Analysis Systems

1982 ◽  
Vol 61 (s109) ◽  
pp. 34-34
Author(s):  
Samuel J. Agronow ◽  
Federico C. Mariona ◽  
Frederick C. Koppitch ◽  
Kazutoshi Mayeda
Keyword(s):  
Statistical Analysis ◽  
Data Base ◽  
Genetic Data ◽  
Data Set ◽  
Multiple Data

Noninvasive patient tracker mask for spinal 3D navigation: does the required large-volume 3D scan involve a considerably increased radiation exposure?

2020 ◽  
Vol 33 (6) ◽  
pp. 838-844
Author(s):  
Jan-Helge Klingler ◽  
Ulrich Hubbe ◽  
Christoph Scholz ◽  
Florian Volz ◽  
Marc Hohenhaus ◽  
...  
Keyword(s):  
Radiation Exposure ◽  
Image Data ◽  
3D Image ◽  
Flat Panel ◽  
3D Navigation ◽  
Data Set ◽  
Dose Area Product ◽  
Flat Panel Detector ◽  
3D Scan ◽  
Area Product

OBJECTIVEIntraoperative 3D imaging and navigation is increasingly used for minimally invasive spine surgery. A novel, noninvasive patient tracker that is adhered as a mask on the skin for 3D navigation necessitates a larger intraoperative 3D image set for appropriate referencing. This enlarged 3D image data set can be acquired by a state-of-the-art 3D C-arm device that is equipped with a large flat-panel detector. However, the presumably associated higher radiation exposure to the patient has essentially not yet been investigated and is therefore the objective of this study.METHODSPatients were retrospectively included if a thoracolumbar 3D scan was performed intraoperatively between 2016 and 2019 using a 3D C-arm with a large 30 × 30–cm flat-panel detector (3D scan volume 4096 cm3) or a 3D C-arm with a smaller 20 × 20–cm flat-panel detector (3D scan volume 2097 cm3), and the dose area product was available for the 3D scan. Additionally, the fluoroscopy time and the number of fluoroscopic images per 3D scan, as well as the BMI of the patients, were recorded.RESULTSThe authors compared 62 intraoperative thoracolumbar 3D scans using the 3D C-arm with a large flat-panel detector and 12 3D scans using the 3D C-arm with a small flat-panel detector. Overall, the 3D C-arm with a large flat-panel detector required more fluoroscopic images per scan (mean 389.0 ± 8.4 vs 117.0 ± 4.6, p < 0.0001), leading to a significantly higher dose area product (mean 1028.6 ± 767.9 vs 457.1 ± 118.9 cGy × cm2, p = 0.0044).CONCLUSIONSThe novel, noninvasive patient tracker mask facilitates intraoperative 3D navigation while eliminating the need for an additional skin incision with detachment of the autochthonous muscles. However, the use of this patient tracker mask requires a larger intraoperative 3D image data set for accurate registration, resulting in a 2.25 times higher radiation exposure to the patient. The use of the patient tracker mask should thus be based on an individual decision, especially taking into considering the radiation exposure and extent of instrumentation.

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