scholarly journals Visualizing veins from color skin images using convolutional neural networks

2020 ◽  
Vol 13 (04) ◽  
pp. 2050020
Author(s):  
Chaoying Tang ◽  
Yimin Yuan ◽  
Shuhang Xia ◽  
Gehua Ma ◽  
Biao Wang
Keyword(s):  
Deep Learning ◽  
Medical Care ◽  
Near Infrared ◽  
Venous Access ◽  
Color Transfer ◽  
Auxiliary Equipment ◽  
Transfer Scheme ◽  
Medical Treatments ◽  
Vein Access ◽  
Optimization Parameters

Intravenous cannulation is the most important phase in medical practices. Currently, limited literature is available about visibility of veins and the characteristics of patients associated with difficult intravenous access. In modern medical treatment, a major challenge is locating veins for patients who have difficult venous access. Presently, some products of vein locators are available in the market to improve vein access, but they need auxiliary equipment such as near infrared (NIR) illumination and camera, which add weight and cost to the devices, and cause inconveniences to daily medical care. In this paper, a vein visualization algorithm based on the deep learning method was proposed. Based on a group of synchronous RGB/NIR arm images, a convolutional neural network (CNN) model was designed to implement the mapping from RGB to NIR images, where veins can be detected from skin. The model has a simple structure and less optimization parameters. A color transfer scheme was also proposed to make the network adaptive to the images taken by smartphone in daily medical treatments. Comprehensive experiments were conducted on three datasets to evaluate the proposed method. Subjective and objective evaluations showed the effectiveness of the proposed method. These results indicated that the deep learning-based method can be used for visualizing veins in medical care applications.

scholarly journals Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 742
Author(s):  
Canh Nguyen ◽  
Vasit Sagan ◽  
Matthew Maimaitiyiming ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Viral Diseases ◽  
Support Vector ◽  
Spectral Features ◽  
Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

scholarly journals Molecular imaging and deep learning analysis of uMUC1 expression in response to chemotherapy in an orthotopic model of ovarian cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hongwei Zhao ◽  
Hasaan Hayat ◽  
Xiaohong Ma ◽  
Daguang Fan ◽  
Ping Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Ovarian Cancer ◽  
Deep Learning ◽  
Cancer Progression ◽  
In Vivo Imaging ◽  
Near Infrared ◽  
Response To Therapy ◽  
Response To Chemotherapy

Abstract Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we utilized AI for monitoring the expression of underglycosylated mucin 1 (uMUC1) tumor antigen, a biomarker for ovarian cancer progression and response to therapy, using contrast-enhanced in vivo imaging. This was done using a dual-modal (magnetic resonance and near infrared optical imaging) uMUC1-specific probe (termed MN-EPPT) consisted of iron-oxide magnetic nanoparticles (MN) conjugated to a uMUC1-specific peptide (EPPT) and labeled with a near-infrared fluorescent dye, Cy5.5. In vitro studies performed in uMUC1-expressing human ovarian cancer cell line SKOV3/Luc and control uMUC1low ES-2 cells showed preferential uptake on the probe by the high expressor (n = 3, p < .05). A decrease in MN-EPPT uptake by SKOV3/Luc cells in vitro due to uMUC1 downregulation after docetaxel therapy was paralleled by in vivo imaging studies that showed a reduction in probe accumulation in the docetaxel treated group (n = 5, p < .05). The imaging data were analyzed using deep learning-enabled segmentation and quantification of the tumor region of interest (ROI) from raw input MRI sequences by applying AI algorithms including a blend of Convolutional Neural Networks (CNN) and Fully Connected Neural Networks. We believe that the algorithms used in this study have the potential to improve studying and monitoring cancer progression, amongst other diseases.

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals Liberalni odgovori na problem uskraćivanja zdravstvene skrbi djeci i maloljetnicima iz religijskih razloga

JAHR ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 43-60
Author(s):  
Ivan Cerovac ◽  
Maša Dunatov
Keyword(s):  
Medical Care ◽  
The State ◽  
Children's Health ◽  
Medical Treatments ◽  
Liberal Feminism ◽  
Liberal Egalitarianism ◽  
The World ◽  
Liberal Multiculturalism ◽  
State Interventions ◽  
Religious Reasons

Medical experts, both in Croatia and in the world, are facing nowadays an increasing number of cases where the parents refuse, because of certain religious reasons, medical care and certain medical treatments for their children, even though those treatments could preserve the children’s health or even save their lives. The parents are convinced that they are acting with good intentions and in child’s favour, which leads to certain problems regarding the regulation of these cases, as well as to disagreements regarding the rights of parents and their children, or the legitimacy of state interventions in this sphere. This paper puts forward four possible liberal solutions to the above described problem (liberal archipelago, liberal multiculturalism, liberal egalitarianism and liberal feminism), specifies the scope of legitimate interventions by the state that these theories allow, and reviews the advantages of each position, as well as the most important objections directed toward each.

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