A Multi-dataset Approach for DME Risk Detection in Eye Fundus Images

2020 ◽  
pp. 285-298
Author(s):  
Catarina Carvalho ◽  
João Pedrosa ◽  
Carolina Maia ◽  
Susana Penas ◽  
Ângela Carneiro ◽  
...  
Keyword(s):  
Fundus Images ◽  
Risk Detection

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.

The Influence of Potentially Fraudulent Reports on Audit Risk Assessment and Planning

2001 ◽  
Vol 76 (1) ◽  
pp. 59-80 ◽  
Author(s):  
D. Paul Newman ◽  
Evelyn Patterson ◽  
Reed Smith
Keyword(s):  
Financial Reporting ◽  
Planning Process ◽  
Dynamic Interaction ◽  
Fraudulent Financial Reporting ◽  
Expected Payoff ◽  
Audit Risk ◽  
Audit Planning ◽  
Inherent Risk ◽  
Risk Detection ◽  
Reported Earnings

We consider how auditors assess the risk of fraudulent financial reporting and plan their audit where a possibly fraudulent auditee anticipates the assessment and planning process. The auditor uses the auditee's (possibly fraudulent) earnings report to revise his beliefs about the likelihood of fraud when formulating an audit plan. We find that as underlying earnings increase, a fraudulent auditee increases reported earnings. In turn, as the auditee's reported earnings increase, the auditor increases audit effort. We also find that the auditee (who knows the auditor will use the report for audit planning) selects reports that increase his own expected payoff, relative to reports he would select if the auditor did not observe the report before finalizing the audit plan. By contrast, the auditor is no better off using the auditee's report for audit planning. Inherent risk, detection risk, and overall audit risk can increase when the auditor uses the auditee's report. Thus, because of the dynamic interaction between the auditor and auditee, procedures that aid in assessing audit risk may not reduce that risk or result in more efficient audits.

scholarly journals Significance of pepsinogen in screening for gastric intestinal metaplasia in Guangdong, China

2021 ◽  
Vol 49 (2) ◽  
pp. 030006052199049
Author(s):  
Xujuan Liu ◽  
Min Zhang ◽  
Riyu Luo ◽  
Keran Mo ◽  
Xingxiang He
Keyword(s):  
Logistic Regression ◽  
Intestinal Metaplasia ◽  
Single Factor ◽  
Antibody Testing ◽  
Cross Sectional ◽  
Gastric Intestinal Metaplasia ◽  
Risk Detection ◽  
Tree Models ◽  
Single Factor Analysis ◽  
Serological Biomarkers

Objective Diagnosis of gastric intestinal metaplasia (GIM) relies on gastroscopy and histopathologic biopsy, but their application in screening for GIM is limited. We aimed to identify serological biomarkers of GIM via screening in Guangdong, China. Methods Cross-sectional field and questionnaire data, demographic information, past medical history, and other relevant data were collected. Blood samples were collected for pepsinogen (PG)I, PGII, gastrin-17, and Helicobacter pylori antibody testing, and gastroscopy and histopathologic biopsy were performed. Single factor and logistic regression analyses were used to evaluate the correlation between these indicators and GIM, and decision tree models were used to determine the cut-off points between indicators. Results Of 443 participants enrolled, 87 (19.6%) were diagnosed with GIM. Single factor analysis showed that pepsin indicators (PGI, PGII, and PGI/PGII ratio) and the factors Mandarin as native language, urban residency, hyperlipidemia, and age were associated with GIM. Logistic regression analysis showed that PGI and age were associated with GIM. Conclusions Age is an important factor for predicting GIM progression; age >60 years increased its risk. Detection of GIM was higher in individuals with PGI levels >127.20 ng/mL, which could be used as a threshold indicating the need to perform gastroscopy and histopathologic biopsy.

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