Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/526778
Title: Detection of Diabetic Retinopathy by using Artificial Intelligence Technique
Researcher: Kumar, Krishan
Guide(s): Sharma, Vikrant
Keywords: Engineering
Engineering and Technology
Engineering Multidisciplinary
University: GNA University
Completed Date: 2023
Abstract: Hierarchical fuzzy expert system, an ease to use interface has been designed by using symptoms of diseases which consists of twenty one input parameters and fuzzy if-else rules are constructed by using medical expert advice in order to build very precise fuzzy inference system. Fuzzy rules and membership functions are constructed and followed by fine tuning to obtain a very efficient performance. This system can help the medical specialists for the early diagnosis of diabetic retinopathy. This system can also be used as a classifier to differentiate the types of diabetic retinopathy. The hierarchical system contains medical parameters based on the fuzzy rules designed in the system, the system give an accurate result. After comparing the fuzzy rules with health care experts, the hierarchical fuzzy system was found to be 0.99% accuracy, 0.98% sensitivity and 100% specificity respectively. newlineSecondly the fundus image positive and negative images for Non proliferative diabetic retinopathy and proliferative diabetic retinopathy recorded under standard image acquisition protocol are considered for this work. To increase the quality of the image, pre-processing is done using a image normalization, median and image embedded (Adaptive Histogram Equalization). Active Counter Model was used to perform segmentation and then feature extraction was done by using first order statistical features of the images. At the last step, classification had been done with the help of the mean values. Based on the calculations the newlinesensitivity of the system was calculated as 0.98%, with 0.97% of specificity and 0.97% of accuracy. newlineFurther, three different classification techniques namely Decision Tree (Adaptive Histogram Equalization), Naïve Bayes and Support Vector Machine (SVM) has been applied on training and test dataset to detect diabetic retinopathy. newlineThe designed system has been validated by conducting various trials in hospitals of Jalandhar region. The results have been verified by the medical specialists.
Pagination: i-xvii, 131
URI: http://hdl.handle.net/10603/526778
Appears in Departments:Department of Electronics and Communication Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File185.8 kBAdobe PDFView/Open
02_prelim pages.pdf863.57 kBAdobe PDFView/Open
03_content.pdf422.11 kBAdobe PDFView/Open
04_abstract.pdf250.33 kBAdobe PDFView/Open
05_chapter 1.pdf11.52 MBAdobe PDFView/Open
06_chapter 2.pdf11.53 MBAdobe PDFView/Open
07_chapter 3.pdf11.52 MBAdobe PDFView/Open
08_chapter 4.pdf11.54 MBAdobe PDFView/Open
09_chapter 5.pdf11.52 MBAdobe PDFView/Open
10_annexures.pdf7.68 MBAdobe PDFView/Open
80_recommendation.pdf122.9 kBAdobe PDFView/Open
Show full item record


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: