Celiac disease diagnosis from videocapsule endoscopy images with residual learning and deep feature extraction

2020 ◽  
Vol 187 ◽  
pp. 105236 ◽  
Author(s):  
Xinle Wang ◽  
Haiyang Qian ◽  
Edward J. Ciaccio ◽  
Suzanne K. Lewis ◽  
Govind Bhagat ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Celiac Disease ◽  
Disease Diagnosis ◽  
Residual Learning ◽  
Deep Feature ◽  
Videocapsule Endoscopy ◽  
Deep Feature Extraction ◽  
Celiac Disease Diagnosis

Towards Robust Localization Deep Feature Extraction by CNN

2020 ◽  
Author(s):  
Erik Carlbaum ◽  
Sina Sharif Mansouri ◽  
Christoforos Kanellakis ◽  
Anton Koval ◽  
George Nikolakopoulos
Keyword(s):  
Feature Extraction ◽  
Deep Feature ◽  
Robust Localization ◽  
Deep Feature Extraction

Smart paddy field monitoring system using deep learning and IoT

2021 ◽  
pp. 1063293X2198894
Author(s):  
Prabira Kumar Sethy ◽  
Santi Kumari Behera ◽  
Nithiyakanthan Kannan ◽  
Sridevi Narayanan ◽  
Chanki Pandey
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Transfer Learning ◽  
Paddy Field ◽  
New Technology ◽  
Support Vector ◽  
Nitrogen Status ◽  
Deep Feature ◽  
Per Capita ◽  
Deep Feature Extraction

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

scholarly journals LOW BONE MINERAL DENSITY IN BRAZILIAN PATIENTS AT DIAGNOSIS OF CELIAC DISEASE

2015 ◽  
Vol 52 (3) ◽  
pp. 176-179 ◽  
Author(s):  
Joyce Timmermans Pires da SILVA ◽  
Renato M NISIHARA ◽  
Luís Roberto KOTZE ◽  
Márcia OLANDOSKI ◽  
Lorete Maria da Silva KOTZE
Keyword(s):  
Bone Mineral Density ◽  
Celiac Disease ◽  
Bone Mineral ◽  
Disease Diagnosis ◽  
Bone Fragility ◽  
Mineral Density ◽  
X Ray ◽  
Low Bone Mineral Density ◽  
Celiac Disease Diagnosis ◽  
Male Subjects

BackgroundLow bone mineral density is considered an extra-intestinal manifestation of celiac disease with reduced bone mass, increased bone fragility, and risk of fractures. Celiac disease is considered a condition at high risk for secondary osteoporosis and the evaluation of bone density is very important in the clinical management of these patients.ObjectiveThe present study aimed to investigate bone alterations in celiac patients from Curitiba, South Region of Brazil at diagnosis, correlating the findings with age and gender.MethodsPatients who were included in the study were attended to in a private office of the same physician from January 2009 to December 2013. The diagnosis of celiac disease was done through clinical, serological and histological findings. All data were collected from the medical charts of the patients. After the diagnosis of celiac disease, evaluation for low bone mineral density was requested by dual-energy X-ray absorptiometry (DEXA). DEXA bone densitometer was used to estimate low bone mineral density at the lumbar spine and femur.ResultsA total of 101 patients, 82 (81.2%) female and 19 (18.8%) male subjects, with mean age of 39.0±3.03 years were included. At celiac disease diagnosis, 36 (35.6%) were younger than 30 years, 41 (40.6%) were between 31 and 50 years, and 24 (23.8%) were older than 50 years. Among the evaluated patients, 69 (68.3%) presented low bone mineral density, being 47% with osteopenia and 32% with osteoporosis. Patients who were older than 51 years and diagnosed with celiac disease presented low bone mineral density in 83.3% (20/24) of the cases. As expected, age influenced significantly the low bone mineral density findings. Among women, low bone mineral density was present with high frequency (60%) from 30 to 50 years. In patients diagnosed older than 60 years (n=8), all the women (n=5) and two of the three men had osteoporosis.ConclusionThis study demonstrated that 69% of Brazilian patients with celiac disease at diagnosis had low bone mineral density, being more frequent in women older than 50 years.

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