Open AccessDetection of Proliferative Diabetic Retinopathy in Fundus Images Using Convolution Neural Network
Author(s) -
H. Hassan,
Marzuqi Yaakob,
Sasni Ismail,
Juwairiyyah Abd Rahman,
Izyani Mat Rusni,
Azlee Zabidi,
Ihsan Mohd Yassin,
Nooritawati Md Tahir,
Suraiya M. Shafie
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/769/1/012029
Subject(s) - fundus (uterus) , convolutional neural network , artificial intelligence , computer science , diabetic retinopathy , artificial neural network , pattern recognition (psychology) , deep learning , convolution (computer science) , feature extraction , data set , ophthalmology , medicine , diabetes mellitus , endocrinology
Convolution Neural Network (CNN) is one of the techniques under Artificial Neural Network (ANN) used to develop a Deep Learning Neural Network (DLNN) algorithm for detection of Proliferative Diabetic Retinopathy (PDR) on the fundus images. About 116 PDR and 150 Non-Proliferative Diabetic Retinopathy (NPDR) of fundus images retrieved from the publicly available MESSIDOR database applied in this research. This study consisted three objectives that included the execution of two pre-processing techniques on the data-set which were resizing and normalizing the fundus images, developed deep learning operational Artificial Intelligence (AI) network of feature extraction algorithm for detection of PDR on the fundus images and determined the output classification of the network encompassing the accuracy, sensitivity and specificity. There were five different parameters carried out along this research. Here, Parameter 5 showed the best performance among the five parameters based on the value of accuracy, sensitivity, and specificity that was 73.81%, 76%, and 69% respectively.