Retinal Fundus Biometric Analysis for Personal Identifications

2008 ◽  
pp. 1229-1237 ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Lucia Cariello ◽  
Donatello Columbo ◽  
Domenico Daleno ◽  
Massimiliano Dellisanti Fabiano ◽  
...  
Keyword(s):  
Biometric Analysis ◽  
Retinal Fundus

Diagnose Glaucoma in Retinal fundus Images

2017 ◽  
Author(s):  
Javedkhan Y. Pathan ◽  
Dr.Pramod Patil
Keyword(s):  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Two-Tier Grading System for NPDR Severities of Diabetic Retinopathy in Retinal Fundus Images

2020 ◽  
Vol 14 ◽  
Author(s):  
Charu Bhardwaj ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Analysis ◽  
Early Stage ◽  
Image Feature ◽  
Grading System ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus ◽  
Timely Treatment

: Diabetic Retinopathy is the leading cause of vision impairment and its early stage diagnosis relies on regular monitoring and timely treatment for anomalies exhibiting subtle distinction among different severity grades. The existing Diabetic Retinopathy (DR) detection approaches are subjective, laborious and time consuming which can only be carried out by skilled professionals. All the patents related to DR detection and diagnoses applicable for our research problem were revised by the authors. The major limitation in classification of severities lies in poor discrimination between actual lesions, background noise and other anatomical structures. A robust and computationally efficient Two-Tier DR (2TDR) grading system is proposed in this paper to categorize various DR severities (mild, moderate and severe) present in retinal fundus images. In the proposed 2TDR grading system, input fundus image is subjected to background segmentation and the foreground fundus image is used for anomaly identification followed by GLCM feature extraction forming an image feature set. The novelty of our model lies in the exhaustive statistical analysis of extracted feature set to obtain optimal reduced image feature set employed further for classification. Classification outcomes are obtained for both extracted as well as reduced feature set to validate the significance of statistical analysis in severity classification and grading. For single tier classification stage, the proposed system achieves an overall accuracy of 100% by k- Nearest Neighbour (kNN) and Artificial Neural Network (ANN) classifier. In second tier classification stage an overall accuracy of 95.3% with kNN and 98.0% with ANN is achieved for all stages utilizing optimal reduced feature set. 2TDR system demonstrates overall improvement in classification performance by 2% and 6% for kNN and ANN respectively after feature set reduction, and also outperforms the accuracy obtained by other state of the art methods when applied to the MESSIDOR dataset. This application oriented work aids in accurate DR classification for effective diagnosis and timely treatment of severe retinal ailment.

Survival of classic swine fever virus in hams made from the meat of pigs vaccinated with the PAV-250 strain and unvaccinated pigs

2019 ◽  
Vol 10 (3) ◽  
pp. 536-551
Author(s):  
Heidi Amezcua Hempel ◽  
María Salud Rubio Lozano ◽  
Eliseo Manuel Hernández Baumgarten ◽  
Pablo Correa Girón † ◽  
Oscar Torres Ángeles ◽  
...  
Keyword(s):  
Reference Strain ◽  
Clinical Signs ◽  
Classical Swine Fever ◽  
Fever Virus ◽  
Direct Immunofluorescence ◽  
Biometric Analysis ◽  
Treatment Groups ◽  
Group A ◽  
Group B ◽  
Group D

The study was to determine the presence of Classical Swine Fever virus (CSFv), in the meat of vaccinated pigs with the PAV-250 strain and then challenged using the same strain. Five treatment groups were established (each with four pigs). Group A: Pigs thatwere fed with processed hams from negative animals; Group B: Pigs that were fed with processed hams from commercial pigs inoculated with the ALD (reference strain) (titre of 104.0/ml); Group C: Pigs fed with processed hams from pigs infected with the virulent ALD strain (titre of 102.5/ml); Group D: Pigs fed with processed hams from pigs vaccinated with the PAV-250 strain and challenged with the ALD strain (titre of 101.1/ml); and Group E: Pigs fed with processed hams from pigs vaccinated with two doses of the PAV-250 strain and challenged with the ALD strain (negative). Blood samples were taken at d 1, 5, 10, 15 and 20 for biometric analysis. Groups B, C and D manifested clinical signs of CSFv: 40 °C temperature, anorexia, paralysis, vomiting, diarrhea, tremor, hirsute hair and cyanosis. Pigs were slaughtered and necropsies performed to identify lesions in tissues. Results of direct immunofluorescence testing of tissues were positive and the virus was recovered. Under these study conditions, it was found that CSFv resisted the cooking method at 68 °C for 40 min in hams from unvaccinated pigs, and that the virus was able to transmit the disease to healthy unvaccinated pigs, whereas the hams from the vaccinated animals did not transmit the virus.

scholarly journals Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ling-Ping Cen ◽  
Jie Ji ◽  
Jian-Wei Lin ◽  
Si-Tong Ju ◽  
Hong-Jie Lin ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Characteristic Curve ◽  
Weighted Average ◽  
Age Related Macular Degeneration ◽  
Learning Platform ◽  
Age Related ◽  
Public Data ◽  
Primary Test ◽  
Retinal Fundus

AbstractRetinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.

scholarly journals Understanding the Emotional Impact of GIFs on Instagram through Consumer Neuroscience

2021 ◽  
Vol 11 (8) ◽  
pp. 108
Author(s):  
Idoia Rúa Hidalgo ◽  
Maria Galmes-Cerezo ◽  
Carmen Cristofol-Rodríguez ◽  
Irene Aliagas
Keyword(s):  
Social Media ◽  
Emotional Valence ◽  
Marketing Strategies ◽  
Emotional Impact ◽  
Media Campaigns ◽  
Biometric Analysis ◽  
Neuroscience Research ◽  
Explicit Measure ◽  
Two Phases ◽  
Emotional Level

The ability of GIFs to generate emotionality in social media marketing strategies is analyzed. The aim of this work is to show how neuroscience research techniques can be integrated into the analysis of emotions, improving the results and helping to guide actions in social networks. This research is structured in two phases: an experimental study using automated biometric analysis (facial coding, GSR and eye tracking) and an analysis of declared feelings in the comments of Instagram users. Explicit valence, type of emotion, length of comment and proportion of emojis are extracted. The results indicate that the explicit measure of emotional valence shows a higher and more positive emotional level than the implicit one. This difference is influenced differently by the engagement and the proportion of emojis in the comment. A further step has been taken in the measurement of user emotionality in social media campaigns, including not only content analysis, but also providing new insights thanks to neuromarketing.

scholarly journals Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Jiangang Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Geometric Transformation ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Classification Problems ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

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