Author:
Abu Hassan Hasliza,Yaakob Marzuqi,Ismail Sasni,Abd Rahman Juwairiyyah,Mat Rusni Izyani,Zabidi Azlee,Mohd Yassin Ihsan,Md. Tahir Nooritawati,Mohamad Shafie Suraiya
Abstract
Abstract
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.
Reference28 articles.
1. Visualisation of exudates in fundus images using radar chart and color auto correlogram technique;Hassan,2015
2. Classification Of Visualization Exudates Fundus Images Results Using Support Vector Machine Classification Of Visualization Exudates Fundus Images Results Using Support Vector Machine Classification Of Visualization Exudates Fundus Images Results;Hassan;J Fundam Appl Sci,2017
3. Automated optic disc removal in fundus images using iterative heuristics and morphological operations;Hassan,2013
4. History of diabetes mellitus;Ahmed;Saudi Med. J.,2002
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献