Case-Based Expert System for Early Detection of Diabetic Retinopathy

2021 ◽  
pp. 259-267
Author(s):  
Rahul Barman ◽  
Saroj Kumar Biswas ◽  
Dolly Das ◽  
Biswajit Purkayastha ◽  
Malaya Dutta Borah
Keyword(s):  
Diabetic Retinopathy ◽  
Expert System ◽  
Early Detection ◽  
Case Based

Design Fuzzy Expert System And Certainty Factor In Early Detection Of Stroke Disease

2020 ◽  
Author(s):  
Laurentinus ◽  
Kiswanto ◽  
Rahmat Sulaiman ◽  
Fransiskus Panca Juniawan ◽  
Dwi Yuny Sylfania ◽  
...  
Keyword(s):  
Expert System ◽  
Early Detection ◽  
Fuzzy Expert System ◽  
Certainty Factor

INTEGRATING FUZZY C-MEANS AND MAHALANOBIS METRIC CLASSIFICATION FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGING

2015 ◽  
Vol 15 (05) ◽  
pp. 1550085 ◽  
Author(s):  
MADHURI TASGAONKAR ◽  
MADHURI KHAMBETE
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Ground Truth ◽  
Vital Role ◽  
Detection Accuracy ◽  
Fuzzy C Means ◽  
Fundus Imaging ◽  
Imaging Detection ◽  
Retinal Structure

Diabetes affects retinal structure of a diabetic patient by generating various lesions. Early detection of these lesions can avoid the loss of vision. Automation of detection process can be made easily feasible to masses by the use of fundus imaging. Detection of exudates is significant in diabetic retinopathy (DR) as they are earlier signs and can cause blindness. Finding the exact location as well as correct number of exudates play vital role in the overall treatment of a patient. This paper presents an algorithm for automatic detection of exudates for DR. The algorithm combines the advantages of supervised and unsupervised techniques. It uses fuzzy-C means (FCM) segmentation on coarse level and mahalanobis metric for finer classification of segmented pixels. Mahalanobis criterion gives significance to most relevant features and thus proves a better classifier. The results are validated using DIARETDB0 and DIARETDB1 databases and the ground truth provided with it. This evaluation provided 95.77% detection accuracy.

Early detection of diabetic retinopathy from big data in hadoop framework

Displays ◽  
2021 ◽  
Vol 70 ◽  
pp. 102061
Author(s):  
Amartya Hatua ◽  
Badri Narayan Subudhi ◽  
Veerakumar T. ◽  
Ashish Ghosh
Keyword(s):  
Big Data ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Hadoop Framework

Graphical Fault Diagnosis Expert System of CNC Machine Tool Based on B/S

2014 ◽  
Vol 945-949 ◽  
pp. 1707-1712
Author(s):  
Bin Shen ◽  
Shu Yu Zhao ◽  
Jia Hai Wang ◽  
Juergen Fleischer
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Machine Tool ◽  
Case Based Reasoning ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Rule Based ◽  
Detailed Case ◽  
Case Based ◽  
Remote Fault Diagnosis

Based on the authors previous work of developing an expert system for fault diagnosis of CNC machine tool, this paper studied the theory and method of CNC remote fault diagnosis expert system based on B/S, and presents schema and structure of the expert system in detailed. Case based reasoning is used for the multi-alarm diagnosis, and rule based reasoning is used for single-alarm diagnosis. At last fault diagnosis expert system was designed and developed making use of C# and ASP.NET.

scholarly journals Diabetic Vitrectomy

2020 ◽  
Author(s):  
Ogugua N. Okonkwo
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Visual Impairment ◽  
Surgical Techniques ◽  
Surgical Removal ◽  
Disease Monitoring ◽  
Advanced Stage ◽  
Improved Outcomes ◽  
Diabetic Vitrectomy ◽  
Proliferative Disease

Diabetic retinopathy (DR) in its advanced stage is a leading cause of blindness and visual impairment. Despite efforts at early detection of DR, disease monitoring, and medical therapy, significant proportions of people living with diabetes still progress to develop the advanced proliferative disease, which is characterized by neovascularization, actively proliferating fibrovascular membranes, and retinal traction. The surgical removal of this proliferating tissue and the treatment of the retinal ischemic drive can be very rewarding, providing significant stability of the retina and in several cases improved retinal anatomy and vision. Diabetic vitrectomy comprises a broad range of surgical techniques and maneuvers, which offer the surgeon and patient opportunity to reverse deranged vitreoretinal anatomy and improve or stabilizes vision. Advances in vitreoretinal technology have contributed greatly to more recent improved outcomes; it is expected that future advances will offer even more benefit.

scholarly journals Automated Early Detection of Diabetic Retinopathy

Ophthalmology ◽  
2010 ◽  
Vol 117 (6) ◽  
pp. 1147-1154 ◽  
Author(s):  
Michael D. Abràmoff ◽  
Joseph M. Reinhardt ◽  
Stephen R. Russell ◽  
James C. Folk ◽  
Vinit B. Mahajan ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection

Using Expert Systems and Cloud Computing to Build a Diabetes Diagnosis System

2014 ◽  
Vol 651-653 ◽  
pp. 2063-2066
Author(s):  
Chung C. Chang ◽  
Shu Hui Tsai
Keyword(s):  
Cloud Computing ◽  
Diabetic Retinopathy ◽  
Expert System ◽  
Expert Systems ◽  
System Design ◽  
Research Method ◽  
Research Topic ◽  
Prototype System ◽  
Diabetes Diagnosis ◽  
Cloud Platform

This study combines an expert system with cloud computing, establishing the expert system on a cloud platform to provide users with assistance and recommendations about diabetes and diabetic retinopathy diagnosis. This study mainly adopts an empirical approach. The first step is to define the research topic, and then propose questions and the research purpose based on the research background and motivation. Research results are related to three areas, specifically diabetes and diabetic retinopathy, expert systems, and cloud computing. After analyzing and organizing the literature, the research method and scope of research are established with a system design based on the three areas. This study then develops a prototype system to validate, evaluate, and test the expert system. Finally, study gives the conclusion and recommendations.

Feature extraction from retinal fundus image for early detection of diabetic retinopathy

2013 ◽  
Author(s):  
Syna Sreng ◽  
Jun-Ichi Takada ◽  
Noppadol Maneerat ◽  
Don Isarakorn ◽  
Ruttikorn Varakulsiripunth ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus
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